Methods and systems of prioritizing treatments, vaccination, testing and/or activities while protecting the privacy of individuals

ABSTRACT

An aspect of some embodiments of the invention relates to system and methods for anonymously selecting subjects for treatment against an infectious disease caused by a pathogen, comprising: 1. a plurality of electronic devices configured with instructions to generate an ID, when in proximity of another such electronic device, one or both of transmit said ID to said another electronic device and receive an ID from said another electronic device, generating a score based on a plurality of such received IDs, receiving information from a server, displaying relevant treatment instructions to said subjects based on received information; 2. at least one server comprising instructions for sending to said plurality of electronic devices information to display said relevant treatment instructions; where said at least one server or said electronic devices comprise instructions to generate a prediction of likelihood of a subject transmitting said pathogen, based on a score of the subject.

RELATED APPLICATIONS

This application claims the benefit of priority of Israel PatentApplication No. 277083 filed on Sep. 1, 2020, Israel Patent ApplicationNo. 276665 filed on Aug. 11, 2020, and Israel Patent Application No.276648 filed on Aug. 11, 2020. The contents of the above applicationsare all incorporated by reference as if fully set forth herein in theirentirety.

This application is also related to United Arab Emirates PatentApplication No. P6001304/2020 filed on Sep. 17, 2020, the contents ofwhich are incorporated herein by reference in their entirety.

FIELD AND BACKGROUND OF THE INVENTION

The present invention, in some embodiments thereof, relates to methodsand systems of prioritizing vaccinations\treatments\testing and, moreparticularly, but not exclusively, to method and systems of prioritizingvaccinations\treatments\testing in a pandemic situation, wherebyvaccines are at short supply and while protecting the privacy of theindividuals in the population.

Coronavirus disease 2019 (COVID-19) is an infectious disease caused bysevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It wasfirst identified in December 2019 in Wuhan, Hubei, China, and hasresulted in an ongoing pandemic. The first confirmed case has beentraced back to 17 Nov. 2019 in Hubei. As of 6 August 2020, more than18.7 million cases have been reported across 188 countries andterritories, resulting in more than 706,000 deaths. More than 11.3million people have recovered. The virus is primarily spread betweenpeople during close contact, most often via small droplets produced bycoughing, sneezing, and talking. The droplets usually fall to the groundor onto surfaces rather than travelling through air over long distances.However, the transmission may also occur through smaller droplets thatare able to stay suspended in the air for longer periods of time inenclosed spaces, as typical for airborne diseases. Less commonly, peoplemay become infected by touching a contaminated surface and then touchingtheir face. It is most contagious during the first three days after theonset of symptoms, although spread is possible before symptoms appear,after they disappear and from people who show very mild or do not showsymptoms at all.

In addition, about 5% of COVID-19 patients experience complicationsincluding septic shock, acute respiratory distress syndrome (ARDS),acute cardiac or kidney injury, and disseminated intravascularcoagulation (DIC). These complications are thought to be manifestationsof the cytokine storm triggered by the host immune response of thevirus. In critically ill patients, ARDS was the most common complicationin 67% of the patients with a 28-day mortality of 61.5%. DIC has beenwidely reported in COVID-19. Pulmonary embolism (PE) in COVID-19patients has been reported in a few studies. A recent study pointed to ahigher incidence of PE with 23% in severe COVID-19 patients. Therelationship between virally triggered inflammation, venousthromboembolism, and ARDS in COVID-19 is still under investigation.Given that patients with severe COVID-19 often present with shortness ofbreath and pulmonary infiltrates, the diagnosis of PE may be overlookedin the context of an ARDS diagnosis.

A research article by Straetemans et. al. called “Prioritizationstrategies for pandemic influenza vaccine in 27 countries of theEuropean Union and the Global Health Security Action Group: a review”discussed vaccine prioritization strategies during pandemic times, butits conclusions are limited to the critical groups, for example, healthcare providers (e.g., doctors, nurses, laboratories, hospitals, etc.),essential service providers (e.g., police, fire fighters, public sectorpersonnel, governmental personnel, etc.) and high risk individuals(e.g., people with high risk of complications, pregnant women, children,etc.). These obvious groups usually amount to less than 2-10% of thetotal population, which still leaves the government with the question ofwhat is the best order to vaccinate the rest of the population, namelyprioritizing vaccinations.

SUMMARY OF THE INVENTION

Following is a non-exclusive list including some examples of embodimentsof the invention. The invention also includes embodiments, which includefewer than all the features in an example, and embodiments usingfeatures from multiple examples, also if not expressly listed below.

Example 1. An anonymized method of treating subjects against aninfectious disease caused by a pathogen, comprising:

a. providing an electronic device with proximity tracking circuitry foreach of said subjects;

b. generating an ID for each said electronic device;

c. at a proximity event, when a particular said electronic device of aparticular said subject is in proximity of one or more other of saidelectronic devices, one or both of transmitting said ID or an indicationthereof to said one or more other devices and receiving an ID orindication thereof from said one or more other devices, by saidparticular electronic device;

d. generating, by said particular electronic device a score reflecting apropensity for proximity, according to a plurality of received IDs;

e. generating for said particular electronic device a prioritization oftreatment based on said score;

f. treating said particular subject according to said prioritization.

Example 2. The method according to example 1, wherein said generating anID comprises generating an ID having fewer than 100,000 potentialvalues.

Example 3. The method according to example 2, wherein said generating anID comprises generating a unique ID and also generating said ID as aportion of said unique ID.

Example 4. The method according to example 1, further comprisingchanging said ID periodically.

Example 5. The method according to example 1, further comprisinggenerating a second ID and transmitting said second ID or indicationthereof together with said ID.

Example 6. The method according to example 5, wherein said transmittinga second ID is carried out only at a fraction of said proximity events.

Example 7. The method according to example 6, wherein said transmittingcomprises transmitting also second IDs previously received from othersof said electronic devices.

Example 8. The method according to example 6, comprising generating anindication of closeness of a population met by said electronic devicebased on said received second IDs.

Example 9. The method according to example 1, wherein said score dependson an estimation of propensity of proximity of said one or more otherdevices.

Example 10. The method according to example 1, wherein said generatingsaid score comprises counting the number of received IDs.

Example 11. The method according to example 10, wherein said countingcomprises counting unique IDs.

Example 12. The method according to example 10, wherein said countingcomprises counting IDs with a weighted parameter, said weightedparameter is generated by analyzing said exchanged second IDs.

Example 13. The method according to example 1, wherein said generatingfor said particular device comprises transmitting said score to a serverand generating said prioritization on said server.

Example 14. The method according to example 13, wherein generating saidprioritization comprises comparing scores by different ones of saidelectronic devices.

Example 15. The method according to example 1, wherein said generatingfor said particular device comprises generating said prioritization onsaid particular electronic device.

Example 16. The method according to example 15, wherein said generationcomprises receiving form a server a list or a function indicationprioritization according to score.

Example 17. The method according to example 1, comprising displayingtreatment instructions on said particular electronic device based onsaid generated prioritization.

Example 18. The method of example 1, wherein said pathogen comprises acorona virus and wherein said treatment comprises a vaccination andwherein said prioritization is used to select subjects at greater riskof transmitting the pathogen during a pandemic to be vaccinated soonerthan subjects less likely to transmit the pathogen.

Example 19. A system for anonymously selecting subjects for treatmentagainst an infectious disease caused by a pathogen, comprising:

a. a plurality of electronic devices configured to be carried around bysaid subjects and configured with instructions to:

-   -   i. generate an ID comprising for each said electronic device;    -   ii. when in proximity of another such electronic device, one or        both of transmit said ID or an indication thereof to said        another electronic device and receive an ID or indication        thereof from said another electronic device;    -   iii. generating, a score based on a plurality of such received        IDs;    -   iv. receiving information from a server;    -   v. displaying relevant treatment instructions to said subjects        based on said received information;

b. at least one server comprising a memory and a plurality of modules;said memory-comprising instructions for:

-   -   vi. sending to said plurality of electronic devices information        usable by a circuitry in said plurality of electronic devices to        display said relevant treatment instructions,        wherein said at least one server or said electronic devices        comprise instructions to generate a prediction of likelihood of        a subject transmitting said pathogen, based on a score of the        subject.

Example 20. The system according to example 19, wherein said informationcomprises one or more of subject specific information.

Example 21. The system according to example 19, wherein said informationcomprises general information usable by a plurality of subjects anddevices thereof.

Example 22. The system according to example 19, wherein said server isconfigured with instructions to receive anonymous scores for a pluralityof said electronic devices and use said received scores to generate saidgeneral information, said electronic devices configured to use saidgeneral information to determine a relative treatment priority for theirrespective subjects.

Example 23. The system according to example 19, wherein said electronicdevices comprises a proximity-detecting module using one or more of:

a. physical proximity data received by means of electronic positioningdata of said subject;b. a distance indicating sensor which indicates physical proximity ofthe location of a device in relation to the location of said anotherdevice; andc. historical location data.

Example 24. The system according to example 19, wherein said at leastone server or said electronic devices comprise instructions to determinea treatment prioritization based on said likelihood.

Example 25. The system according to example 23, wherein said determine atreatment prioritization further comprises one or more of:

a. generating a score component based on a nature of a location wheresaid physical proximity data is related;b. generating a score component comprising health data of the subject ofone or both electronic devices;c. generating a score component comprising a profession of the subjectof one or both electronic devices;d. generating a score component reflecting relative health risk to saidsubject if said subject contracts said pathogen; ande. generating a score component reflecting damage to society if saidsubject contracts said pathogen.

Example 26. The system according to example 23, wherein when saidphysical proximity data is related to a location that is either indoorsor in a closed space, then said predicted likelihood of said subject oftransmitting said pathogen increases by a factor of between about 10times to about 100 times.

Example 27. The system according to example 19, further comprising avaccination server, which allocates vaccinations for a corona virusaccording to, said displayed treatment information.

Example 28. The system according to example 27, wherein said servercomprises a simulation module configured to perform one or both of:

(a) predict the effect of vaccination on disease spread;(b) predict the effect of an ID transmission probability ondistinguishing between subjects who contact mainly subjects in a samesubpopulation.

Example 29. The system of example 19, wherein said electronic devicesare configured to transmit a second ID and previously received secondIDs, at a probability of less than 10% and using said received secondIDs to generate said score. Example 30. The system of example 19,wherein said transmitted ID is a non-unique ID having fewer possiblevalues than 10% of the number of said devices.

According to an aspect of some embodiments of the present inventionthere is provided a method of selecting subjects for beingvaccinated/treated against an infectious disease caused by a pathogen,using personal physical proximity information of a subject, comprising:

a. generating, by circuitry, a predicted likelihood of said subject oftransmitting said pathogen based on said physical proximity information,for a plurality of subjects;

b. selecting subjects of said plurality of subjects forvaccinating/treating based on a prediction that saidvaccinating/treating said subjects will reduce a likelihood of spreadingof said disease in said plurality of subjects, wherein said selecting isbased on said generated predicted likelihood.

According to some embodiments of the invention, said pathogen isselected from the group consisting of a virus, a bacterium, a fungus anda protozoan.

According to some embodiments of the invention, said disease is endemicor pandemic.

According to some embodiments of the invention, said predictedlikelihood of said subject of transmitting said pathogen comprises oneor more score components used for generating a score.

According to some embodiments of the invention, said score relates to apredicted likelihood of a group of subjects transmitting said pathogenbased on said physical proximity information, and said physicalproximity information is a first score component used for saidgenerating said score.

According to some embodiments of the invention, said generating saidscore further comprises a score component based on a nature of alocation where said physical proximity information is related.

According to some embodiments of the invention, said nature of thelocation is one or more of an open space, a closed space, indoor,outdoor, ventilated indoor space, non-ventilated indoor space and anycombination thereof.

According to some embodiments of the invention, when said physicalproximity information is related to a location that is either indoors orin a closed space, then said predicted likelihood of said subject oftransmitting said pathogen increases by a factor of between about 10times to about 100 times.

According to some embodiments of the invention, said physical proximityinformation is physical proximity data received by means of electronicpositioning data of said subject.

According to some embodiments of the invention, said physical proximityinformation is physical proximity data of the location of said subjectin relation to the location of other subjects.

According to some embodiments of the invention, said physical proximitydata comprises one or more of physical proximity distance data, durationof physical proximity data and/or ambience of physical proximity data.

According to some embodiments of the invention, said electronicpositioning data is one or more of electronic geographical positioningdata of said subject, electronic proximity positioning data of saidsubject relative to other subjects.

According to some embodiments of the invention, said method furthercomprises generating a predicted likelihood of said subject contractingsaid pathogen based on said physical proximity data.

According to some embodiments of the invention, said generating a scorefurther comprises a second score component based on said predictedlikelihood of said subject contracting said pathogen based on saidphysical proximity data.

According to some embodiments of the invention, said electronicpositioning data is collected using one or more electronic devices.

According to some embodiments of the invention, said one or moreelectronic devices are one or more of a smartphone, a tablet, asmartwatch and a dedicated electronic device.

According to some embodiments of the invention, the method furthercomprising vaccinating/treating said subjects according to said score.

According to some embodiments of the invention, said generating a scorefurther comprises a third score component reflecting relative healthrisk to said subject if said subject contracts said pathogen.

According to some embodiments of the invention, said generating a scorefurther comprises a fourth score component reflecting damage to societyif said subject contracts said pathogen.

According to some embodiments of the invention, said electronicpositioning data comprises geographical location data.

According to some embodiments of the invention, said physical proximityinformation comprises historical location data.

According to some embodiments of the invention, said generating saidscore further comprises a component comprising historical health data.

According to some embodiments of the invention, said generating saidscore further comprises a component comprising a profession in record ofsaid subject.

According to some embodiments of the invention, said physical proximityinformation further comprises information received from a third party.

According to some embodiments of the invention, said physical proximityinformation is provided by said subject actively.

According to some embodiments of the invention, said physical proximityinformation is provided by said subject passively by means of said oneor more electronic devices.

According to some embodiments of the invention, said pathogen is avirus.

According to some embodiments of the invention, said virus is a coronavirus.

According to some embodiments of the invention, said virus is SARS-CoV.

According to some embodiments of the invention, said virus is MERS-CoV.

According to some embodiments of the invention, said virus isSARS-CoV-2.

According to some embodiments of the invention, said virus is aninfluenza virus.

According to some embodiments of the invention, said disease results ininfluenza like symptoms.

According to an aspect of some embodiments of the present inventionthere is provided a method of selecting subjects for beingvaccinated/treated against an infectious disease caused by a pathogen,comprising:

a. automatically collecting physical proximity information of a subjectwith other subjects;

b. generating a predicted likelihood of said subject of transmittingsaid virus based on said physical proximity information;

c. generating a score comprising a first score component based on saidpredicted likelihood of said subject of transmitting said virus;

d. repeating steps b-c for a plurality of subjects; and

e. prioritizing vaccination/treatment of said subjects according to saidscore.

According to some embodiments of the invention, said pathogen isselected from the group consisting of a virus, a bacterium, a fungus anda protozoan.

According to some embodiments of the invention, said disease is endemicor pandemic.

According to some embodiments of the invention, said generating saidscore further comprises a score component based on a nature of alocation where said physical proximity information is related.

According to some embodiments of the invention, said nature of thelocation is one or more of an open space, a closed space, indoor,outdoor, ventilated indoor space, non-ventilated indoor space and anycombination thereof.

According to some embodiments of the invention, when said physicalproximity information is related to a location that is either indoors orin a closed space, then said predicted likelihood of said subject oftransmitting said pathogen increases by a factor of between about 10times to about 100 times.

According to some embodiments of the invention, said physical proximityinformation is physical proximity data received by means of electronicpositioning data of said subject.

According to some embodiments of the invention, said physical proximityinformation is physical proximity data of the location of said subjectin relation to the location of other subjects.

According to some embodiments of the invention, said physical proximitydata comprises one or more of physical proximity distance data, durationof physical proximity data and/or ambience of physical proximity data.

According to some embodiments of the invention, said electronicpositioning data is one or more of electronic geographical positioningdata of said subject, electronic proximity positioning data of saidsubject relative to other subjects.

According to some embodiments of the invention, said method furthercomprises generating a predicted likelihood of said subject contractingsaid pathogen based on said physical proximity data.

According to some embodiments of the invention, said generating a scorefurther comprises a second score component based on said predictedlikelihood of said subject contracting said pathogen based on saidphysical proximity data.

According to some embodiments of the invention, said electronicpositioning data is collected using one or more electronic devices.

According to some embodiments of the invention, said one or moreelectronic devices are one or more of a smartphone, a tablet, asmartwatch and a dedicated electronic device.

According to some embodiments of the invention, the method furthercomprising vaccinating/treating said subjects according to said score.

According to some embodiments of the invention, said generating a scorefurther comprises a third score component reflecting relative healthrisk to said subject if said subject contracts said pathogen.

According to some embodiments of the invention, said generating a scorefurther comprises a fourth score component reflecting damage to societyif said subject contracts said pathogen.

According to some embodiments of the invention, said electronicpositioning data comprises geographical location data.

According to some embodiments of the invention, said physical proximityinformation comprises historical location data.

According to some embodiments of the invention, said generating saidscore further comprises a component comprising historical health data.

According to some embodiments of the invention, said generating saidscore further comprises a component comprising a profession in record ofsaid subject.

According to some embodiments of the invention, said physical proximityinformation further comprises information received from a third party.

According to some embodiments of the invention, said physical proximityinformation is provided by said subject actively.

According to some embodiments of the invention, said physical proximityinformation is provided by said subject passively by means of said oneor more electronic devices.

According to some embodiments of the invention, said pathogen is avirus.

According to some embodiments of the invention, said virus is a coronavirus.

According to some embodiments of the invention, said virus is SARS-CoV.

According to some embodiments of the invention, said virus is MERS-CoV.

According to some embodiments of the invention, said virus isSARS-CoV-2.

According to some embodiments of the invention, said virus is aninfluenza virus.

According to some embodiments of the invention, said disease results ininfluenza like symptoms.

According to an aspect of some embodiments of the present inventionthere is provided a system for selecting subjects for beingvaccinated/treated against an infectious disease caused by a pathogen,comprising:

a. at least one server comprising a memory;

b. an analytics module;

c. a database module;

d. a simulation module;

said memory in said at least one server comprising instructions, saidinstructions comprising:

-   -   i. generating, by circuitry, a predicted likelihood of said        subject of transmitting said pathogen based on said physical        proximity information, for a plurality of subjects;    -   ii. selecting subjects of said plurality of subjects for        vaccinating/treating based on a prediction that said        vaccinating/treating said subjects will reduce a likelihood of        spreading of said disease in said plurality of subjects, wherein        said selecting is based on said generated predicted likelihood.

According to some embodiments of the invention, said pathogen isselected from the group consisting of a virus, a bacterium, a fungus anda protozoan.

According to some embodiments of the invention, said disease is endemicor pandemic.

According to some embodiments of the invention, said predictedlikelihood of said subject of transmitting said pathogen comprises oneor more score components used for generating a score.

According to some embodiments of the invention, said score relates to apredicted likelihood of a group of subjects transmitting said pathogenbased on said physical proximity information, and said physicalproximity information is a first score component used for saidgenerating said score.

According to some embodiments of the invention, said generating saidscore further comprises a score component based on a nature of alocation where said physical proximity information is related.

According to some embodiments of the invention, said nature of thelocation is one or more of an open space, a closed space, indoor,outdoor, ventilated indoor space, non-ventilated indoor space and anycombination thereof.

According to some embodiments of the invention, when said physicalproximity information is related to a location that is either indoors orin a closed space, then said predicted likelihood of said subject oftransmitting said pathogen increases by a factor of between about 10times to about 100 times.

According to some embodiments of the invention, said physical proximityinformation is physical proximity data received by means of electronicpositioning data of said subject.

According to some embodiments of the invention, said physical proximityinformation is physical proximity data of the location of said subjectin relation to the location of other subjects.

According to some embodiments of the invention, said physical proximitydata comprises one or more of physical proximity distance data, durationof physical proximity data and/or ambience of physical proximity data.

According to some embodiments of the invention, said electronicpositioning data is one or more of electronic geographical positioningdata of said subject, electronic proximity positioning data of saidsubject relative to other subjects.

According to some embodiments of the invention, said method furthercomprises generating a predicted likelihood of said subject contractingsaid pathogen based on said physical proximity data.

According to some embodiments of the invention, said generating a scorefurther comprises a second score component based on said predictedlikelihood of said subject contracting said pathogen based on saidphysical proximity data.

According to some embodiments of the invention, said electronicpositioning data is collected using one or more electronic devices.

According to some embodiments of the invention, said one or moreelectronic devices are one or more of a smartphone, a tablet, asmartwatch and a dedicated electronic device.

According to some embodiments of the invention, the system furthercomprising vaccinating/treating said subjects according to said score.

According to some embodiments of the invention, said generating a scorefurther comprises a third score component reflecting relative healthrisk to said subject if said subject contracts said pathogen.

According to some embodiments of the invention, said generating a scorefurther comprises a fourth score component reflecting damage to societyif said subject contracts said pathogen.

According to some embodiments of the invention, said electronicpositioning data comprises geographical location data.

According to some embodiments of the invention, said physical proximityinformation comprises historical location data.

According to some embodiments of the invention, said generating saidscore further comprises a component comprising historical health data.

According to some embodiments of the invention, said generating saidscore further comprises a component comprising a profession in record ofsaid subject.

According to some embodiments of the invention, said physical proximityinformation further comprises information received from a third party.

According to some embodiments of the invention, said physical proximityinformation is provided by said subject actively.

According to some embodiments of the invention, said physical proximityinformation is provided by said subject passively by means of said oneor more electronic devices.

According to some embodiments of the invention, said simulation modulefurther comprises a prediction module.

According to some embodiments of the invention, said pathogen is avirus.

According to some embodiments of the invention, said virus is a coronavirus.

According to some embodiments of the invention, said virus is SARS-CoV.

According to some embodiments of the invention, said virus is MERS-CoV.

According to some embodiments of the invention, said virus isSARS-CoV-2.

According to some embodiments of the invention, said virus is aninfluenza virus.

According to some embodiments of the invention, said disease results ininfluenza like symptoms.

Following is a second non-exclusive list including some examples ofembodiments of the invention. The invention also includes embodiments,which include fewer than all the features in an example, and embodimentsusing features from multiple examples, also if not expressly listedbelow.

Example 1. A method of selecting subjects for being vaccinated againstan infectious disease caused by a pathogen, using personal physicalproximity information of a subject, comprising:

a. generating, by circuitry, a predicted likelihood of said subject oftransmitting said pathogen based on said physical proximity information,for a plurality of subjects;

b. selecting subjects of said plurality of subjects for vaccinatingbased on a prediction that said vaccinating said subjects will reduce alikelihood of spreading of said disease in said plurality of subjects,wherein said selecting is based on said generated predicted likelihood.

Example 2. The method according to example 1, wherein said pathogen isselected from the group consisting of a virus, a bacterium, a fungus anda protozoan.

Example 3. The method according to according to any one of examples 1-2,wherein said disease is endemic or pandemic.

Example 4. The method according to any one of examples 1-3, wherein saidpredicted likelihood of said subject of transmitting said pathogencomprises one or more score components used for generating a score.

Example 5. The method according to example 4, wherein said score relatesto a predicted likelihood of a group of subjects transmitting saidpathogen based on said physical proximity information, and said physicalproximity information is a first score component used for saidgenerating said score.

Example 6. The method according to any one of examples 4-5, wherein saidgenerating said score further comprises a score component based on anature of a location where said physical proximity information isrelated.

Example 7. The method of example 6, wherein said nature of the locationis one or more of an open space, a closed space, indoor, outdoor,ventilated indoor space, non-ventilated indoor space and any combinationthereof.

Example 8. The method according to any one of examples 1-7, wherein whensaid physical proximity information is related to a location that iseither indoors or in a closed space, then said predicted likelihood ofsaid subject of transmitting said pathogen increases by a factor ofbetween about 10 times to about 100 times.

Example 9. The method according to any one of examples 1-8, wherein saidphysical proximity information is physical proximity data received bymeans of electronic positioning data of said subject.

Example 10. The method according to any one of examples 1-9, whereinsaid physical proximity information is physical proximity data of thelocation of said subject in relation to the location of other subjects.

Example 11. The method according to any one of examples 9-10, whereinsaid physical proximity data comprises one or more of physical proximitydistance data, duration of physical proximity data and/or ambience ofphysical proximity data.

Example 12. The method according to any one of examples 9-11, whereinsaid electronic positioning data is one or more of electronicgeographical positioning data of said subject, electronic proximitypositioning data of said subject relative to other subjects.

Example 13. The method according to any one of examples 1-12, whereinsaid method further comprises generating a predicted likelihood of saidsubject contracting said pathogen based on said physical proximity data.

Example 14. The method according to any one of examples 4-13, whereinsaid generating a score further comprises a second score component basedon said predicted likelihood of said subject contracting said pathogenbased on said physical proximity data.

Example 15. The method according to any one of examples 9-14, whereinsaid electronic positioning data is collected using one or moreelectronic devices.

Example 16. The method of example 15, wherein said one or moreelectronic devices are one or more of a smartphone, a tablet, asmartwatch and a dedicated electronic device.

Example 17. The method according to any one of examples 4-16, furthercomprising vaccinating said subjects according to said score.

Example 18. The method according to any one of examples 4-17, whereinsaid generating a score further comprises a third score componentreflecting relative health risk to said subject if said subjectcontracts said pathogen.

Example 19. The method according to any one of examples 4-18, whereinsaid generating a score further comprises a fourth score componentreflecting damage to society if said subject contracts said pathogen.

Example 20. The method according to any one of examples 9-19, whereinsaid electronic positioning data comprises geographical location data.

Example 21. The method according to any one of examples 1-20, whereinsaid physical proximity information comprises historical location data.

Example 22. The method according to any one of examples 4-21, whereinsaid generating said score further comprises a component comprisinghistorical health data.

Example 23. The method according to any one of examples 4-22, whereinsaid generating said score further comprises a component comprising aprofession in record of said subject.

Example 24. The method according to any one of examples 1-23, whereinsaid physical proximity information further comprises informationreceived from a third party.

Example 25. The method according to any one of examples 1-24, whereinsaid physical proximity information is provided by said subjectactively.

Example 26. The method according to any one of examples 1-25, whereinsaid physical proximity information is provided by said subjectpassively by means of said one or more electronic devices.

Example 27. The method according to any one of examples 1-26, whereinsaid pathogen is a virus.

Example 28. The method according to any one of examples 1-27, whereinsaid virus is a corona virus.

Example 29. The method according to any one of examples 1-28, whereinsaid virus is SARS-CoV.

Example 30. The method according to any one of examples 1-28, whereinsaid virus is MERS-CoV.

Example 31. The method according to any one of examples 1-28, whereinsaid virus is SARS-CoV-2.

Example 32. The method according to any one of examples 1-27, whereinsaid virus is an influenza virus.

Example 33. The method according to any one of examples 1-32, whereinsaid disease results in influenza like symptoms.

Example 34. A method of selecting subjects for being vaccinated againstan infectious disease caused by a pathogen, comprising:

a. automatically collecting physical proximity information of a subjectwith other subjects;

b. generating a predicted likelihood of said subject of transmittingsaid virus based on said physical proximity information;

c. generating a score comprising a first score component based on saidpredicted likelihood of said subject of transmitting said virus;

d. repeating steps b-c for a plurality of subjects; and

e. prioritizing vaccination of said subjects according to said score.

Example 35. The method according to example 34, wherein said pathogen isselected from the group consisting of a virus, a bacterium, a fungus anda protozoan.

Example 36. The method according to any one of examples 34-35, whereinsaid disease is endemic or pandemic.

Example 37. The method according to any one of examples 34-36, whereinsaid generating said score further comprises a score component based ona nature of a location where said physical proximity information isrelated.

Example 38. The method according to any one of examples 34-37, whereinsaid nature of the location is one or more of an open space, a closedspace, indoor, outdoor, ventilated indoor space, non-ventilated indoorspace and any combination thereof.

Example 39. The method according to any one of examples 34-38, whereinwhen said physical proximity information is related to a location thatis either indoors or in a closed space, then said predicted likelihoodof said subject of transmitting said pathogen increases by a factor ofbetween about 10 times to about 100 times. Example 40. The methodaccording to any one of examples 34-39, wherein said physical proximityinformation is physical proximity data received by means of electronicpositioning data of said subject.

Example 41. The method according to any one of examples 34-40, whereinsaid physical proximity information is physical proximity data of thelocation of said subject in relation to the location of other subjects.

Example 42. The method according to any one of examples 38-41, whereinsaid physical proximity data comprises one or more of physical proximitydistance data, duration of physical proximity data and/or ambience ofphysical proximity data.

Example 43. The method according to any one of examples 38-42, whereinsaid electronic positioning data is one or more of electronicgeographical positioning data of said subject, electronic proximitypositioning data of said subject relative to other subjects.

Example 44. The method according to any one of examples 38-43, whereinsaid method further comprises generating a predicted likelihood of saidsubject contracting said pathogen based on said physical proximity data.

Example 45. The method according to any one of examples 34-44, whereinsaid generating a score further comprises a second score component basedon said predicted likelihood of said subject contracting said pathogenbased on said physical proximity data.

Example 46. The method according to any one of examples 38-45, whereinsaid electronic positioning data is collected using one or moreelectronic devices. Example 47. The method according to example 46,wherein said one or more electronic devices are one or more of asmartphone, a tablet, a smartwatch and a dedicated electronic device.

Example 48. The method according to any one of examples 34-47, furthercomprising vaccinating said subjects according to said score.

Example 49. The method according to any one of examples 34-48, whereinsaid generating a score further comprises a third score componentreflecting relative health risk to said subject if said subjectcontracts said pathogen.

Example 50. The method according to any one of examples 34-49, whereinsaid generating a score further comprises a fourth score componentreflecting damage to society if said subject contracts said pathogen.

Example 51. The method according to any one of examples 38-50, whereinsaid electronic positioning data comprises geographical location data.

Example 52. The method according to any one of examples 34-51, whereinsaid physical proximity information comprises historical location data.

Example 53. The method according to any one of examples 34-52, whereinsaid generating said score further comprises a component comprisinghistorical health data.

Example 54. The method according to any one of examples 34-53, whereinsaid generating said score further comprises a component comprising aprofession in record of said subject.

Example 55. The method according to any one of examples 34-54, whereinsaid physical proximity information further comprises informationreceived from a third party.

Example 56. The method according to any one of examples 34-55, whereinsaid physical proximity information is provided by said subjectactively.

Example 57. The method according to any one of examples 34-56, whereinsaid physical proximity information is provided by said subjectpassively by means of said one or more electronic devices.

Example 58. The method according to any one of examples 34-57, whereinsaid pathogen is a virus.

Example 59. The method according to any one of examples 34-58, whereinsaid virus is a corona virus.

Example 60. The method according to any one of examples 34-58, whereinsaid virus is SARS-CoV.

Example 61. The method according to any one of examples 34-58, whereinsaid virus is MERS-CoV.

Example 62. The method according to any one of examples 34-58, whereinsaid virus is SARS-CoV-2.

Example 63. The method according to any one of examples 1-57, whereinsaid virus is an influenza virus.

Example 64. The method according to any one of examples 1-63, whereinsaid disease results in influenza like symptoms.

Example 65. A system for selecting subjects for being vaccinated againstan infectious disease caused by a pathogen, comprising:

a. at least one server comprising a memory;

b. an analytics module;

c. a database module;

d. a simulation module;

said memory in said at least one server comprising instructions, saidinstructions comprising:

i. generating, by circuitry, a predicted likelihood of said subject oftransmitting said pathogen based on said physical proximity information,for a plurality of subjects;

ii. selecting subjects of said plurality of subjects for vaccinatingbased on a prediction that said vaccinating said subjects will reduce alikelihood of spreading of said disease in said plurality of subjects,wherein said selecting is based on said generated predicted likelihood.

Example 66. The system according to example 65, wherein said pathogen isselected from the group consisting of a virus, a bacterium, a fungus anda protozoan.

Example 67. The system according to any one of examples 65-66, whereinsaid disease is endemic or pandemic.

Example 68. The system according to any one of examples 65-67, whereinsaid predicted likelihood of said subject of transmitting said pathogencomprises one or more score components used for generating a score.

Example 69. The system according to example 68, wherein said scorerelates to a predicted likelihood of a group of subjects transmittingsaid pathogen based on said physical proximity information, and saidphysical proximity information is a first score component used for saidgenerating said score.

Example 70. The system according to any one of examples 64-69, whereinsaid generating said score further comprises a score component based ona nature of a location where said physical proximity information isrelated.

Example 71. The system of example 70, wherein said nature of thelocation is one or more of an open space, a closed space, indoor,outdoor, ventilated indoor space, non-ventilated indoor space and anycombination thereof.

Example 72. The system according to any one of examples 65-71, whereinwhen said physical proximity information is related to a location thatis either indoors or in a closed space, then said predicted likelihoodof said subject of transmitting said pathogen increases by a factor ofbetween about 10 times to about 100 times.

Example 73. The system according to any one of examples 65-72, whereinsaid physical proximity information is physical proximity data receivedby means of electronic positioning data of said subject.

Example 74. The system according to any one of examples 65-73, whereinsaid physical proximity information is physical proximity data of thelocation of said subject in relation to the location of other subjects.

Example 75. The system according to any one of examples 69-74, whereinsaid physical proximity data comprises one or more of physical proximitydistance data, duration of physical proximity data and/or ambience ofphysical proximity data.

Example 76. The system according to any one of examples 69-75, whereinsaid electronic positioning data is one or more of electronicgeographical positioning data of said subject, electronic proximitypositioning data of said subject relative to other subjects.

Example 77. The system according to any one of examples 65-76, whereinsaid method further comprises generating a predicted likelihood of saidsubject contracting said pathogen based on said physical proximity data.

Example 78. The system according to any one of examples 64-77, whereinsaid generating a score further comprises a second score component basedon said predicted likelihood of said subject contracting said pathogenbased on said physical proximity data.

Example 79. The system according to any one of examples 69-78, whereinsaid electronic positioning data is collected using one or moreelectronic devices.

Example 80. The system according to example 79, wherein said one or moreelectronic devices are one or more of a smartphone, a tablet, asmartwatch and a dedicated electronic device.

Example 81. The system according to any one of examples 64-80, furthercomprising vaccinating said subjects according to said score.

Example 82. The system according to any one of examples 64-81, whereinsaid generating a score further comprises a third score componentreflecting relative health risk to said subject if said subjectcontracts said pathogen.

Example 83. The system according to any one of examples 64-82, whereinsaid generating a score further comprises a fourth score componentreflecting damage to society if said subject contracts said pathogen.

Example 84. The system according to any one of examples 69-83, whereinsaid electronic positioning data comprises geographical location data.

Example 85. The system according to any one of examples 65-84, whereinsaid physical proximity information comprises historical location data.

Example 86. The system according to any one of examples 64-85, whereinsaid generating said score further comprises a component comprisinghistorical health data.

Example 87. The system according to any one of examples 64-86, whereinsaid generating said score further comprises a component comprising aprofession in record of said subject.

Example 88. The system according to any one of examples 65-87, whereinsaid physical proximity information further comprises informationreceived from a third party.

Example 89. The system according to any one of examples 65-88, whereinsaid physical proximity information is provided by said subjectactively.

Example 90. The system according to any one of examples 65-89, whereinsaid physical proximity information is provided by said subjectpassively by means of said one or more electronic devices.

Example 91. The system according to any one of examples 65-90, whereinsaid simulation module further comprises a prediction module.

Example 92. The system according to any one of examples 65-91, whereinsaid pathogen is a virus.

Example 93. The system according to any one of examples 65-92, whereinsaid virus is a corona virus.

Example 94. The system according to any one of examples 65-92, whereinsaid virus is SARS-CoV.

Example 95. The system according to any one of examples 65-92, whereinsaid virus is MERS-CoV.

Example 96. The system according to any one of examples 65-91, whereinsaid virus is SARS-CoV-2.

Example 97. The system according to any one of examples 65-91, whereinsaid virus is an influenza virus.

Example 98. The system according to any one of examples 65-92 whereinsaid disease results in influenza like symptoms.

Unless otherwise defined, all technical and/or scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which the invention pertains. Although methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of embodiments of the invention, exemplarymethods and/or materials are described below. In case of conflict, thepatent specification, including definitions, will control. In addition,the materials, methods, and examples are illustrative only and are notintended to be necessarily limiting.

As will be appreciated by one skilled in the art, some embodiments ofthe present invention may be embodied as a system, method or computerprogram product. Accordingly, some embodiments of the present inventionmay take the form of an entirely hardware embodiment, an entirelysoftware embodiment (including firmware, resident software, micro-code,etc.) or an embodiment combining software and hardware aspects that mayall generally be referred to herein as a “circuit,” “module” or“system.” Furthermore, some embodiments of the present invention maytake the form of a computer program product embodied in one or morecomputer readable medium(s) having computer readable program codeembodied thereon. Implementation of the method and/or system of someembodiments of the invention can involve performing and/or completingselected tasks manually, automatically, or a combination thereof.Moreover, according to actual instrumentation and equipment of someembodiments of the method and/or system of the invention, severalselected tasks could be implemented by hardware, by software or byfirmware and/or by a combination thereof, e.g., using an operatingsystem.

For example, hardware for performing selected tasks according to someembodiments of the invention could be implemented as a chip or acircuit. As software, selected tasks according to some embodiments ofthe invention could be implemented as a plurality of softwareinstructions being executed by a computer using any suitable operatingsystem. In an exemplary embodiment of the invention, one or more tasksaccording to some exemplary embodiments of method and/or system asdescribed herein are performed by a data processor, such as a computingplatform for executing a plurality of instructions. Optionally, the dataprocessor includes a volatile memory for storing instructions and/ordata and/or a non-volatile storage, for example, a magnetic hard-diskand/or removable media, for storing instructions and/or data.Optionally, a network connection is provided as well. A display and/or auser input device such as a keyboard or mouse are optionally provided aswell.

Any combination of one or more computer readable medium(s) may beutilized for some embodiments of the invention. The computer readablemedium may be a computer readable signal medium or a computer readablestorage medium. A computer readable storage medium may be, for example,but not limited to, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, or device, or any suitablecombination of the foregoing. More specific examples (a non-exhaustivelist) of the computer readable storage medium would include thefollowing: an electrical connection having one or more wires, a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), an optical fiber, a portable compact disc read-onlymemory (CD-ROM), an optical storage device, a magnetic storage device,or any suitable combination of the foregoing. In the context of thisdocument, a computer readable storage medium may be any tangible mediumthat can contain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium and/or data usedthereby may be transmitted using any appropriate medium, including butnot limited to wireless, wireline, optical fiber cable, RF, etc., or anysuitable combination of the foregoing.

Computer program code for carrying out operations for some embodimentsof the present invention may be written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Java, Smalltalk, C++ or the like and conventionalprocedural programming languages, such as the “C” programming languageor similar programming languages. The program code may execute entirelyon the user's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Some embodiments of the present invention may be described below withreference to flowchart illustrations and/or block diagrams of methods,apparatus (systems) and computer program products according toembodiments of the invention. It will be understood that each block ofthe flowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

Some of the methods described herein are generally designed only for useby a computer, and may not be feasible or practical for performingpurely manually, by a human expert. A human expert who wanted tomanually perform similar tasks might be expected to use completelydifferent methods, e.g., making use of expert knowledge and/or thepattern recognition capabilities of the human brain, which would bevastly more efficient than manually going through the steps of themethods described herein.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Some embodiments of the invention are herein described, by way ofexample only, with reference to the accompanying drawings. With specificreference now to the drawings in detail, it is stressed that theparticulars shown are by way of example and for purposes of illustrativediscussion of embodiments of the invention. In this regard, thedescription taken with the drawings makes apparent to those skilled inthe art how embodiments of the invention may be practiced.

In the drawings:

FIG. 1 is a schematic illustration of an exemplary definition of asuperspreader, according to some embodiments of the invention;

FIG. 2 is a flowchart of an exemplary embodiment of the invention,according to some embodiments of the invention;

FIG. 3 is a schematic flowchart of a method of calculating a weightedscore, according to some embodiments of the invention;

FIG. 4 is a schematic representation of an exemplary spreading network,according to some embodiments of the invention;

FIGS. 5a-f are flowcharts of exemplary methods for identifyingsuperspreaders with high levels of anonymization, according to someembodiments of the invention;

FIG. 6 is a flowchart of a method of generating a score, according tosome embodiments of the invention; and

FIG. 7 is a schematic representation of an exemplary system, accordingto some embodiments of the invention.

DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION

The present invention, in some embodiments thereof, relates to methodsand systems of prioritizing vaccination/treatment and, moreparticularly, but not exclusively, to methods and systems ofprioritizing vaccination/treatment in a pandemic situation.

Overview

A broad aspect of some embodiments of the invention relates to reduce apandemic by reducing a k value of infection in addition to and/or at theexpense of reducing an R0 value thereof. In some embodiments of theinvention, this is achieved by identifying and vaccinating (or otherwisepreventing infection by) persons who are potential super spreaders(e.g., people who, on the average, are expected to infect more than theaverage, for example, 1, 2, 3 or more or intermediate values of standarddeviations from such average. This may result in effective lowering ofR0 and/or of effective herd immunity. Optionally, people are notmeasured by actual spreading, but rather by characteristics and/orbehavior, which is expected to lead to greater spreading than others.Optionally, such considerations also may be applied to below average inexpected spreading, however, such people usually have a smaller overalleffect on disease spread.

A broad aspect of some embodiments of the invention relates to using aprediction of individual behavior to decide on vaccination priority forthat individual. In some embodiments of the invention, such predictionis based on past behavior of the individual. In some embodiments of theinvention, an individual is given a score used for prioritization. Insome embodiments of the invention, actual prioritization may be based ona determination of the expected effect of such vaccination on spread ofdisease. Optionally, this determination is using a simulation ofpopulation disease spread. In some embodiments of the invention,however, people are evaluated as individuals.

A broad aspect of some embodiments of the invention relates to soft-failof vaccination prioritization, which avoids problems caused by impreciseautomated tracking methods. In some embodiments of the invention, theuse of imperfect information, which, on the one hand does not seriouslydamage the quality of scoring and, on the other hand, can be used tosignificantly increase privacy and/or ease of score collection isprovided. It is noted that a mistake, for example, of 4%, 8%, 15% orintermediate percentages in score of an individual or missing apotential super spreader will not have a significantly (e.g., a factorof 2 or more) greater effect on a person (e.g., will not send suchperson into quarantine) and/or the total efficacy of a vaccinationprocess. Also, even after such an effect, it is expected that theoverall result is better than naïve or general classification-basedvaccination prioritization. In some embodiments of the invention,counting of contacts is allowed to be less precise. In some embodimentsof the invention, identification of the quality of the contacts (e.g.,indoor/outdoor, coughing behavior, actual proximity and/or existence ofprotective factors) is allowed to be reduced and optionally carried outusing less precise sensing means as provided, for example, bycellphones. Optionally or additionally, the identification of uniquecontacts is allowed to be less precise.

An aspect of some embodiments of the invention relates to prioritizingvaccinations and/or prophylactic treatments in a pandemic event byidentifying potential superspreaders. In some embodiments, potentialsuperspreaders are identified from a population before critical groupshave been excluded. In some embodiments, potential superspreaders areidentified from a population after critical groups have been excluded.In some embodiments, critical groups are for example, health careproviders, essential service provides and high-risk individuals. In someembodiments, potential superspreaders are identified according to one ormore of: their usual and/or expected level of activity, their usualand/or expected type of activity, their usual and/or expected healthstate, their belonging to a closed or open circle of connections, thekind of individuals a certain subject usually meets, the kind ofindividuals a certain subject has met and their actual sensed behavior.In some embodiments, the entire population (with or without the criticalgroups), or a part of the population, such as a critical group or othergroup, are subjected to an analysis which provides each individual witha “superspreader score” (referred hereinafter just as “score”) whichreflects a likelihood of such a person acting as a superspreader and/orgeneral expected ability of that person to spread the disease. In someembodiments, potential superspreaders are identified according to ascore in relation to other scores from the rest of the population. Insome embodiments, potential superspreaders are identified according to ascore in relation to a predetermined score generated by the system. Insome embodiments, identified potential superspreaders having the highestscore are vaccinated (or provided with prophylactic treatments) first.It should be appertained that the score may also be weighted with otherinformation, such as criticality for infrastructure, social standingand/or risk form the disease or perceived risk to high-value members ofsociety.

An aspect of some embodiments of the invention relates to prioritizingvaccinations and/or prophylactic treatments in a pandemic eventaccording to a potential level of danger to the society. In someembodiments, the invention relates identification of individuals that,in case they were in a phase of infecting others with an infectiousdisease/virus/pathogen, it would potentially put everyone else indanger. For example, in the case where a subject is in potential contactwith other people and those other people potentially meet a high numberof individuals. For example, a subject that interacts face to face withhealth provider personnel, but does not belong to the health providesnetwork. If that subject becomes infected, he/she can potentially infecta high number of health provider personnel, which will then,potentially, spread the infectious disease/virus/pathogen to a largerpopulation.

An aspect of some embodiments of the invention relates to protecting theprivacy of individuals in a population when their information is usedfor prioritizing vaccinations and/or prophylactic treatments in apandemic event, optionally also according to a potential level of dangerto the society. In some embodiments, actual names of individuals areencrypted and/or anonymized in the system. In some embodiments, only adevice of an individual comprises the capabilities to translate betweenthe actual name of the individual and the encrypted/anonymized username. In some embodiments, the servers of the system comprise highlevels of protection and/or encryption for the information storedtherein. In some embodiments of the invention, even the device of theuser stores a minimum of identifiable information, such as a score, butdoes not stores actual identities of persons met.

In some embodiments of the invention, private information about aperson's activity and/or persons they came in contact with and/orgeolocations are maintained on that person's mobile device and used todetermine a priority for that person (e.g., by assessing the number ofcontacts and overall risk of spreading disease due to typical behaviorof that person). Optionally, the mobile device is used to broadcast,optionally in an anonymous manner, the score, so that, it may bedetermined, for example, by a central computer, the distribution ofscores across the population. It should be noted that the actualidentification of the device and/or user is not needed, just the numberof persons with each score, so this can be taken into account togetherwith number and/or availability of vaccine doses, to plan a best dosingschedule. Optionally, the mobile device will receive a predeterminedscale of scores from the system, which will be then used by the mobiledevice to translate the score in view of the scale of scores andcommunicate the user to get treatment and, optionally, the when andwhere.

In some embodiments of the invention, once calculated, such dosingschedule is broadcasted and each device can apply its score to theschedule to determine a priority, which is given to the device owner.Optionally, when arriving for a scheduled vaccination, the device owneris required to show that code and, optionally, proof that the telephonebelongs to them.

In one example, the local device calculates a score based on a user'smedical information and behavior. Optionally is also receives behaviorof those that person meets (e.g., transmitted to the device atproximity/contact of devices of those people). In some embodiments, theinformation is stored without identification of source, except possiblya hash code, which, while can be used to detect that a certain devicewas “met”, it cannot be used to identify the device. In someembodiments, once this score (e.g., risk of contagion) is calculated,the broadcasted information regarding number of vaccinations availableand/or number of persons in each class is noted. In some embodiments,this data may be used to determine which vaccination priority thepersonal device score merits, for example, in the same manner as wouldbe by a central computer (e.g., all scores above x, where there are ypeople with a score above x and y is the number of available vaccines).

In some embodiments of the invention, broadcasts and data transmissionsare digitally signed to prevent tampering. This has a potentialadvantage of allowing more anonymous transmission method to be used(e.g., Tor).

It should be noted that additionally or alternatively to a centralprocessing, the calculation of the vaccine priority may be distributedbetween some or all of the mobile devices, for example, usingparallelization methods known in the art, which optionally also preventsignificant amount of information from passing through any particulardevice.

In some embodiments of the invention, the device calculates the priorityand determines when the device owner should be vaccinated, treatedand/or tested. For example, the number and duration of persons inproximity to the device can be used to calculate a risk score.Optionally, medical information, such as susceptibility and/or risk ofspreading by coughing is downloaded to the device. This is typically nota significant breach of anonymity, as the identity of the device istypically known to the medical record provider. In some embodiments ofthe invention, a person can apply to receive a rating, for example,based on importance, job (e.g., healthcare provider), being part ofcritical infrastructure and/or risk of death. Such rating may beprovided in the form of a one-time code, which the person can enter intothe device. In this manner, the device can increase or decrease the riskscore and/or priority of vaccination, without any central authoritybeing aware of the person's activities.

In some embodiments of the invention, as the device calculates theperson's score, it may generate warning to the device owner to avoid orreduce certain behavior. Optionally, such warning is tied to reductionin priority if not heeded. Optionally or additionally, the manuallyentered rating may affect such warnings. For example, sociallypromiscuous activity by a doctor may not merit such warning and/or maynot reduce the doctor's score (at least while activity is performed atan allowed location, such as a hospital, which location may be indicatedas part of the rating), but will generate a warning or a sanction (e.g.,if not heeded) to a person without such rating.

In some embodiments of the invention, when deciding if to allow entry ofa person into a crowded location, such as a sports arena or a shoppingmall, a user may be required to show their rating.

A potential benefit of some embodiments of the invention is that ratherthan give out vaccination to critical workers, while placing the rest ofsociety in a lockdown (e.g., complete or semi or otherwiserestrictions), the total risk of spread may be reduced with a same orsmaller number of vaccine doses.

A potential benefit of some embodiments of the invention isself-policing. If a person does not install suitable software fortracking movements, such person may receive a lower priority oftreatment/vaccination. Similarly, if a person leaves their device off,then such off-time can be noted and used to affect the score, or evencan be used as an indication that that person is not at risk.

In some embodiments of the invention, a process of using the methodincludes:

(a) Learning the behavior of individuals. This may be done, for example,using existing contact tracking methods and/or using methods asdiscussed herein.

Optionally, such learned behavior is maintained in privacy and/orcollected in an anonymous manner or processed as it is collected, topreserve anonymity.

(b) Scoring, which can be based, for example, on number, variety and/orquality of contacts, degree of bridging between subpopulations, risk toindividual, risk to others the individual is in contact with, otherfacts that affect spreading (e.g., chronic cough) and/or existingimmunity.

(c) Inviting the individual to be vaccinated, optionally though softwareon an electronic device used for contact tracking.

(d) Vaccination, optionally verified using the software to identify theperson being vaccinated.

An aspect of some embodiments of the invention relates to identifyingpotential superspreaders without the use of personal data. In someembodiments, superspreaders are identified by providing an anonymous IDto each individual, for example, when a dedicated application/software(referred hereinafter as “application” or “app”) is installed in anelectronic device. In some embodiments, IDs are exchanged betweenelectronic devices when in proximity to each other (e.g., to indicate apotentially infectious “meeting” of the device holders). In someembodiments, what is transmitted is only a part of such ID (or anindication thereof), which potentially decreases the chances to identifythe specific user. In some embodiments of the invention, even thepartial IDs substantially unique (e.g., a random number with morepossibilities than the number of expected meetings). In some embodimentsof the invention, the partial ID is selected to be non-unique, forexample, including only 100, 1000, 10,000 or intermediate or smaller orgreater possibilities. In some embodiments, prioritizing vaccinationsand/or prophylactic treatments in a pandemic event is performedaccording to a superspreader score calculated by the number of IDscollected by each user.

An aspect of some embodiments of the invention relates to the quality ofpeople an individual meets. In some embodiments of the invention,meeting with a person can be given a higher or lower weight, based onwhether that person is himself a super spreader and/or tends to meetsuper spreaders and/or tends to meet others form many sub-populations.In some embodiments of the invention, when two devices meet, theyexchange their own score and/or number of contacts or other information,which is used to generate an indication of how much of a potentialsuperspreader that person is. In some embodiments, such people may begiven a higher weight. Optionally or additionally, persons who are froma same subpopulation and/or which have fewer contacts and/or which aremet more often, are given a lower weight.

An aspect of some embodiments of the invention relates to assessing thedegree of contacts inside a subpopulation and between subpopulations.Society often has bubbles (subpopulations) within which there is a lotof contact within the bubble but considerably less contact betweenbubbles. In such a context, a person who bridges between bubbles may bea greater threat of disease spread than a person with more overallcontacts but most or all within the bubble. In some embodiments of theinvention, a method is provided for assessing the degree to which aperson is within bubbles and/or bridges between bubbles or betweennon-bubble subpopulations. For example, the method may be used todistinguish between a first person where 90% of their contacts arewithin a strongly connected sub-group vs. a person where only 10% oftheir contacts are to a same strongly sub-group vs a person where 90% ofcontacts are to a strongly connected sub-group, but there are multiple(exclusive) such subgroups.

In some embodiments of the invention, a distributed method of assessingthe degree to which contacts of an individual are within a stronglyconnected or other type of bubble, is provided. An alternative view ofsuch method is assessing a degree of diffusion, which may be correlatedwith a degree of propagation of disease.

In some embodiments of the invention, some or all individuals areassigned a second (or more) ID which is transferred to people they meetat a probability lower than 100%. Optionally, when two individuals meetthey exchange not only their second ID, but also all second IDs theyhave collected. As with a regular ID, the second or further IDs may bemore or less unique. When an individual device assesses the second IDsit collected, it will tend to have fewer IDs if it is within a bubble(e.g., because it will mainly have IDs within the bubble) than if itinterconnects bubbles (e.g., in which case it can have IDs from multiplebubbles). Optionally, the number of second IDs is used as a measure ofdiffusion of IDs in the contact network. In some embodiments of theinvention, the transfer of second IDs can be weighted (and/orprobability of transfer adjusted), for example, to better model thelikely of transfer of disease, for example, weighted higher for IDscollected in closed spaces, at close distances or IDs received from adevice owned by a person with a chronic cough and/or less if owner isknown (e.g., recorded as such) to be careful with facemasks or otherprotective gear. Such weighting may be used additionally oralternatively also for the other scores described herein. The score maybe normalized to the period in which the score is collected. Suchnormalization may be alternatively or additionally applied to scorebased on the first ID. The normalization may be non-linear (e.g., thescore may increase faster at early times) and may be different fordifferent IDs and/or for different individual characteristic values.

In some embodiments of the invention, the probability of transfer ispreset (e.g., 0.01%, 0.1%, 1%, 10% or intermediate or smaller or greaterpercentages). Optionally or additionally, multiple additional IDs areprovided, each one transferred at a different probability. Optionally,the preset probability is determined using a simulation. It is notedthat with a very small transfer probability, there may not be sufficientdiffusion of second ID values, while with a large probability, allindividuals will collect all second IDs, given enough time. For example,a simulation of a contact network may be run with different presettransfer values to detect a value which allows to distinguish betweentypical sub-population sizes and/or which, within the measurementperiod, does not reflect diffusion of substantially all second IDs allover the network. Similarly, the degree of uniqueness of the second IDmay be selected using such a simulation to ensure that the probabilityof a same second ID reaching an individual from two different subgroupsis sufficiently low (e.g., below 10%).

An aspect of some embodiments of the invention relates to the politicalissues involved in vaccination prioritization. In some embodiments ofthe invention, using an objective measure of risk due to behavior allowsvaccination selection without (or less) a political fiat of selectinggroups and/or reducing political pressure applied to prefer a particulargroup, as the individuals are treated by prioritization software asindividuals and do are not identified as or treated as belonging toparticular groups. Also within a particular group, using an automatedvaccination prioritization method can be used to reduce friction andargument.

An aspect of some embodiments of the invention relates to encouragingusers to use a dedicated application/software for tracking contacts (andoptionally identifying potential superspreaders either anonymized ornot) by providing vaccinations and/or prophylactic treatments first tothose individuals that use the dedicated software. In some embodiments,individuals that use the dedicated software are those individuals thatcontribute to the overall benefit of the population, therefore areprovided with vaccinations and/or prophylactic treatments before thosewho not.

Before explaining at least one embodiment of the invention in detail, itis to be understood that the invention is not necessarily limited in itsapplication to the details of construction and the arrangement of thecomponents and/or methods set forth in the following description and/orillustrated in the drawings and/or the Examples. The invention iscapable of other embodiments or of being practiced or carried out invarious ways.

Definition of the Population

During a pandemic, once a valid vaccine/prophylactic drug becomesavailable, and the number of vaccines/drug doses is limited or not allavailable at the same time, the government must decide who will receivefirst the vaccine/prophylactic treatment. According to studies,governments decide to provide the first doses of the treatment to thegroup of individuals that belong to:

a) Health care services, for example doctors, nurses, laboratories,hospitals, etc.;

b) Essential service services, for example police, fire fighters, publicsector personnel, governmental personnel, etc.; and

c) High risk individuals, for example people with high risk ofcomplications, pregnant women, children, etc.

These individuals belong to a group called critical groups, due to thenature of their activity or due to their health status during pandemictimes. Usually, critical groups amount to about 2% to about 10% of thetotal population of a country.

After the critical groups have been vaccinated and/or providedprophylactic treatments, since the number of vaccinations/treatments islimited, there is the question who should be vaccinated/treated next.This is generally true also within a critical group or other groupchosen for vaccination, for example, a group of less at riskindividuals, such as males aged 50-60.

In some embodiments, the population is defined as a number ofindividuals between about 10 individuals and about 100 individuals,optionally between about 100 individuals and about 1,000 individuals,optionally between about 1,000 individuals and about 1,000,000individuals, optionally up to 10,000,000, optionally up to 100,000,000,optionally up to the entire population of earth (e.g., 8 billion).

Principals of Herd Immunity

Before explaining the invention, the notion of herd immunity should beexplained. Herd immunity (also called herd effect, community immunity,population immunity, or social immunity) is a form of indirectprotection from infectious disease that occurs when a large percentageof a population has become immune (resistant) to an infection, whetherthrough vaccination/prophylactic treatment or previous infections,thereby providing a measure of protection for individuals who are notimmune. In a population in which a large proportion of individualspossess immunity, such people being unlikely to contribute to diseasetransmission, chains of infection are more likely to be disrupted, whicheither stops or substantially slows the spread of disease. The greaterthe proportion of immune individuals in a community, the smaller theprobability that non-immune individuals will come into contact with aninfectious individual, helping to shield non-immune individuals frominfection. Individuals can become immune by recovering from an earlierinfection or through vaccination/prophylactic treatment. Someindividuals cannot become immune because of medical conditions, such asan immunodeficiency or immunosuppression, and for this group herdimmunity is a crucial method of protection. Once a certain threshold hasbeen reached, herd immunity gradually eliminates a disease from apopulation. This elimination, if achieved worldwide, may result in thepermanent reduction in the number of infections to zero, callederadication. For example, herd immunity created viavaccination/treatment contributed to the eventual eradication ofsmallpox in 1977 and has contributed to the reduction of the frequenciesof other diseases. Herd immunity does not apply to all diseases, justthose that are contagious, meaning that they can be transmitted from oneindividual to another. Tetanus, for example, is infectious but notcontagious, so herd immunity does not apply. Herd immunity wasrecognized as a naturally occurring phenomenon in the 1930s when it wasobserved that after a significant number of children had become immuneto measles, the number of new infections temporarily decreased,including among susceptible children. Mass vaccination/treatment toinduce herd immunity has since become common and proved successful inpreventing the spread of many infectious diseases. One of the mainproblems with achieving herd immunity is that there might be a limitednumber of vaccinations/treatments available to the population and massvaccination/treatment is either not possible or it would take a longtime to achieve herd immunity while the infectious disease continues tospread.

It is a potential benefit of some embodiments of the invention toprovide a method to resolve the problem of who to vaccinate/treat duringa pandemic when a low amount of vaccine/treatment doses are available,while still providing an effective herd immunity, optionally by bettertargeting those individuals likely to pass on disease and vaccinating atleast some of them, in a preferential manner.

Definition of Superspreaders

A superspreader is an unusually contagious organism infected with adisease (infectious disease/virus/pathogen). In the context of ahuman-borne illness, a superspreader is an individual who is more likelyto infect others, compared with a typical infected person.

Some cases of superspreading conform to the 80/20 rule, whereapproximately 20% of infected individuals are responsible for 80% oftransmissions, although superspreading can still be said to occur whensuperspreaders account for a higher or lower percentage oftransmissions. In epidemics with such superspreader events (SSEV), themajority of individuals infect relatively few secondary contacts.

Although loose definitions of superspreader events exist, some efforthas been made at defining what qualifies as a superspreader event(SSEV). Lloyd-Smith et al. (2005) define a protocol to identify asuperspreader event as follows:

a. estimate the effective reproductive number, R, for the disease andpopulation in question;

b. construct a Poisson distribution with mean R, representing theexpected range of Z due to stochasticity without individual variation;

c. define an SSEV as any infected person who infects more than Z(n)others, where Z(n) is the nth percentile of the Poisson(R) distribution.

This protocol defines a 99th-percentile SSEV as a case, which causesmore infections than would occur in 99% of infectious histories in ahomogeneous population. For example, during the SARS-CoV-1 2002-2004SARS outbreak from China, epidemiologists defined a superspreader as anindividual with at least eight transmissions of the disease.Furthermore, superspreaders may or may not show any symptoms of thedisease. In the methods described here, a threshold (or scale) for beinga superspreader may be defined manually and/or determined by analyzingactual contact-transmission data collected manually and/orautomatically.

Putting aside hospitals, private residences and old-age homes, almostall of these superspreader events (SSEVs) took place in the context of(1) parties, (2) face-to-face professional networking events andmeetings, (3) religious gatherings, (4) sports events, (5)meat-processing facilities, (6) ships at sea, (7) singing groups, and(8) funerals.

Factors of Transmission

Superspreaders have been identified who excrete a higher than normalnumber of pathogens during the time they are infectious. This causestheir contacts to be exposed to higher viral/bacterial loads than wouldbe seen in the contacts of non-superspreaders with the same duration ofexposure. This medical information may be available for at least someindividuals, for example, if the epidemic is a recurring one, such asinfluenza. In addition, behavioral and medical attributes may alsoincrease infectivity. For example, a chronic cough (or one due to atemporary disease, which may be noted in a person's medical record) mayincrease the degree to which an individual is contagious. It is notedthat coughs and sneezes (and rate thereof) can be detected automaticallyby a carried device, such as a cellphone, by signal analysis on anautomatically and optionally continually (or repeatedly discrete)collected audio signal form the microphone. It is noted that anindividual's cellphone or other electronic device may have access to aperson medical records, by connecting to an EMR of that individual.

Basic Reproductive Number

The basic reproduction number R0 is the average number of secondaryinfections caused by a typical infective person in a totally susceptiblepopulation. The basic reproductive number is found by multiplying theaverage number of contacts by the average probability that a susceptibleindividual will become infected, which is called the shedding potential.The average number of contacts may further be weighed by quality ofcontact (e.g., length, repetition, distance, protective means and/orairflow quality)

R0=Number of contacts×Shedding potential

Individual Reproductive Number

The individual reproductive number represents the number of secondaryinfections caused by a specific individual during the time thatindividual is infectious. Some individuals have significantly higherthan average individual reproductive numbers and are known assuperspreaders. Through contact tracing, epidemiologists have identifiedsuperspreaders in measles, tuberculosis, rubella, monkeypox, smallpox,Ebola hemorrhagic fever and SARS.

Co-Infections with Other Pathogens

Studies have shown that men with HIV who are co-infected with at leastone other sexually transmitted disease, such as gonorrhea, hepatitis C,and herpes simplex 2 virus, have a higher HIV shedding rate than menwithout co-infection. This shedding rate was calculated in men withsimilar HIV viral loads. Once treatment for the co-infection has beencompleted, the HIV shedding rate returns to levels comparable to menwithout co-infection. Therefore, it could be hypothesized that in caseof viral diseases transmitted through fluids, people with otherpathologies, like chronic coughing, could also be defined assuperspreaders and are optionally so defined, or weighted accordingly insome embodiments of the invention.

Exemplary Pathogens

In some embodiments, a pathogen may be one or more of a virus (in pl.viruses), bacterium (bacteria), fungus (fungi) or a protozoan(protozoa), for example coronavirus (COVID-19, SARS-CoV-1, SARS-CoV-2,MERS-CoV). In some embodiments, the pathogen may be a virus, and saidvirus is an influenza virus. In some embodiments, the disease results ininfluenza like symptoms. It should be understood, that where referred to“virus” and/or “pathogen”, any one of an “infectious disease”, a“generic or specific pathogen”, a “generic or specific virus” areincluded, and the use of the term “virus” and/or “pathogen” is just tofacilitate the explanation and they should include them.

In some embodiments of the invention, the disease is transmitted byrespiratory means, for example, aerosol and/or droplets. Optionally, anelectronic device, such as a cellphone is used to detect contact whichmay be sufficient to transmit (e.g., detecting proximity for example,using Bluetooth power; detecting physical activity for example, buyanalysis of an audio trace recorded from such device; detecting beingindoors or outdoors based on geolocation or based on other sensors onthe cellphone that are affected by being indoors (e.g., echoes inaudio).

Vaccinations and Prophylactic Treatments

In some embodiments, the term vaccination means the administration of avaccine to help the immune system develop protection from a disease. Insome embodiments, vaccines contain a microorganism or virus in aweakened, live or killed state, or proteins or toxins from the organism.In some embodiments, in stimulating the body's adaptive immunity, theyhelp prevent sickness from an infectious disease. In some embodiments,as stated above, when a sufficiently large percentage of a populationhas been vaccinated, herd immunity results.

In some embodiments, the term prophylactic treatment means a preventivemeasure taken to fend off a disease or another unwanted consequence.

In order to facilitate the explanation of the invention, the term“treatment” will be used. It should be understood that when the term“treatment” is used it refers to both vaccinations and prophylactictreatment.

In some embodiments, vaccines are all compounds as disclosed in in thewebsite of the World Health Organization(https://www[dot]who[dot]int/publications/m/item/draft-landscape-of-covid-19-candidate-vaccines),which are all incorporated herein by reference, and which are optionallyprovided (e.g., as a kit) with software such as described herein and/orprovided with instructions for use targeting potential super spreadersdetected, for example, using methods and apparatus as described herein,and include the following:

28 candidate vaccines in clinical evaluation

COVID-19 Route Vaccine Type of Number Timing of Clinical developer/Vaccine candidate of of Admin- Stage manufacturer platform vaccine dosesdoses istration Phase 1 Phase 1/2 Phase 2 Phase 3 University Non-ChAdOx1-S 1 IM PACTR 2020-001228-32 ISR of Oxford/ Replicating202006922165132 CTN AstraZeneca Viral 2020-001072-15 89951424 VectorInterim Report Sinovac Inactivated Inactivated 2 0, 14 IM NCT04383574NCT days NCT04352608 04456595 Wuhan Inactivated Inactivated 2 0, 14 orIM Chi Chi Institute of 0, 21 CTR CTR Biological days 20000318092000034780 Products/ Sinopharm Beijing Inactivated Inactivated 2 0, 14or IM Chi Chi Institute of 0, 21 CTR CTR Biological days 20000324592000034780 Products/ Sinopharm Moderna/ RNA LNP- 2 0, 28 IM NCTNCT04405076 NCT04470427 NIAID encapsulated days 04283461 mRNA InterimReport BioNTech/ RNA 3 LNP- 2 0, 28 IM 2020-001038-36 NCT FosunPharma/mRNAs days Chi 04368728 Pfizer CTR 2000034825 CanSino Non- Adenovirus 1IM Chi Chi Biological Replicating Type 5 CTR CTR Inc./Beijing ViralVector 2000030906 2000031781 Institute of Vector Study Report StudyReport Biotechnology Anhui Protein Adjuvanted 2 or 3 0, 28 IM NCT NCTZhifei Subunit recombinant or 04445194 04466085 Longcom protein 0, 28,Bio- (RBD- 56 days pharmaceutical/ Dimer) Institute of Microbiology,Chinese Academy of Sciences Institute of Inactivated Inactivated 2 0, 28IM NCT NCT Medical days 04412538 04470609 Biology, Chinese Academy ofMedical Sciences Inovio DNA DNA 2 0, 28 ID NCT Pharma- plasmid days04447781 ceuticals/ vaccine NCT International with 04336410 Vaccineelectro- Institute poration Osaka DNA DNA 2 0, 14 IM NCT University/plasmid days 04463472 AnGes/ vaccine + Takara Bio Adjuvant Cadila DNADNA 3 0, 28, lD CTRI/ Healthcare plasmid 56 days 2020/07/026352 Limitedvaccine Genexine DNA DNA 2 0, 28 IM NCT Consortium Vaccine days 04445389(GX-19) Bharat Inactivated Whole- 2 0, 14 IM NCT Biotech Virion days04471519 Inactivated Janssen Non- Ad26COVS1 2 0, 56 IM NCT Pharma-Replicating days 04436276 ceutical Viral Companies Vector NovavaxProtein Full 2 0, 21 IM NCT Subunit length days 04368988 recombinantSARS CoV-2 glycoprotein nanoparticle vaccine adjuvanted with Matrix MKentucky Protein RBD- 2 0, 21 IM NCT Bioprocessing, Subunit based days04473690 Inc Arcturus/ RNA mRNA IM NCT Duke-NUS 04480957 Gamaleya Non-Adeno- 1 IM NCT Research Replicating based 04436471 Institute Viral NCTVector 04437875 Clover Protein Native 2 0, 21 IM NCT Biopharma- Subunitlike days 04405908 ceuticals Inc./ Trimeric GSK/Dynavax subunit SpikeProtein vaccine Vaxine Pty Protein Recombinant 1 IM NCT Ltd/MedytoxSubunit spike 04453852 protein with Advax ™ adjuvant University ProteinMolecular 2 0, 28 IM ACTRN of Subunit clamp days 12620000674932pQueensland/ stabilized CSL/Seqirus Spike protein with MF59 adjuvantInstitute Replicating Measles- 1 or 2 0, 28 IM NCT Pasteur/ Viral vectordays 04497298 Themis/ Vector based (not yet Univ. of recruiting)Pittsburg CVR/Merck Sharp & Dohme Imperial RNA LNP- 2 IM ISRCTN CollegenCoVsaRNA 17072692 London Curevac RNA mRNA 2 0, 28 IM NCT days 04449276People's RNA mRNA 2 0, 14 IM Chi Liberation or 0, CTR Army 28 days2000034112 (PLA) Academy of Military Sciences/ Walvax Biotech. MedicagoVLP Plant- 2 0, 21 IM NCT Inc. derived days 04450004 VLP adjuvanted withGSK or Dynavax adjs. Medigen Protein S-2P 2 0 28 IM NCT Vaccine Subunitprotein + days 04487210 Biologics CpG1018 Corporation/ NIAID/ Dynavax139 candidate vaccines in preclinical evaluation

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In some embodiments, vaccines are all compounds as disclosed in in thewebsite of ClinicalTrials.gov(https://clinicaltrials[dot]gov/ct2/results?cond=COVID-19), which areall incorporated herein by reference. Other vaccines may be used aswell.

In some embodiments, treatment can be the use of Hydroxychloroquine andazithromycin plus zinc.

In some embodiments, vaccines include the vaccine developed by theMoscow-based Gamaleya Institute, named Sputnik-V.

In some embodiments, providing a treatment as disclosed above to healthysubjects can be understood as prophylactic treatment and/or vaccination.

Exemplary Classification of Superspreader

Referring now to FIG. 1, showing a schematic representation of adefinition of superspreader, according to some embodiments of theinvention. In addition to the notion that a superspreader might beidentified as a person who excretes a higher than normal number ofpathogens during the time they are infectious, a superspreader is aperson who may excrete a normal (or low) number of pathogens during thetime they are infectious but this person is potentially and/oreffectively in contact with a high number of people, thereforepotentially infecting the same or more number of people as a person whoexcretes a higher than normal number of pathogens, as schematicallyshown for example in FIG. 1. Following this logic, according to someembodiments of the invention, a superspreader is further identifiedaccording to the number of people he/she can potentially be in contactwith, is expected or estimated to be in contact with (e.g., based onnumber he has been in contact with), no matter the level of excretion ofsaid person.

Super-Spreading Potential Score

In accordance with some embodiments of the invention, there are providedmethods and systems of providing subjects in a population with a“superspreading score”, which will help to provide the order in whichthe subjects, optionally in groups of subjects, will receive treatments.In some embodiments, the higher the score the higher the potential ofeach individual to spread the disease. In some embodiments, the higherthe score, the earlier the individual should receive the treatments. Insome embodiments, a potential advantage of vaccinating/treatingindividuals having the higher superspreading score is to block potentialintersections where a higher number of individuals might be infected bythe potential superspreaders, and this is done by vaccinatingindividuals with potentially and/or actual higher chances to meet otherpeople, and optionally also in relation to other individuals (forexample by normalization of the data). In some embodiments, a potentialadvantage of this method is that a population will potentially reachfaster a state of herd immunity, as the provision of treatmentscontinues.

Referring now to FIG. 2, showing a flowchart of an exemplary embodimentof the invention. In some embodiments, the system and methods are basedon the following: receiving information about a subject 202, analyzingthe received information 204, generating a score 206 based on theinformation, optionally allocating the subject based to the score to ascore group 208, and providing treatment according to the score and/oraccording to the score group 210. As will be shown below, some or all ofthe receiving and generating may be performed on an electronic device ofsubject 202.

Exemplary Factors Influencing the Score

In some embodiments, the score is generated utilizing one or morefactors and/or components, each influencing the final score by eitheradding or subtracting from the score. In some embodiments, the one ormore factors can influence the score in a linear matter(increasing/decreasing the score linearly, for example +1 to the scoreor −2 to the score) and/or one or more factor can affect the score in aweighted matter, as will be further explained below. Exemplary factorsand/or components are one or more of the following:

Profession in Record of the Individual

In some embodiments, the profession of the individual is correlated witha potential number of people the person might be in contact with duringa regular day of operation. In some embodiments, individuals thatpotentially must meet many people due to their profession will receive ahigh score. For example, cashiers at the supermarket, vendors inmarkets, bus drivers, delivery people, technicians, librarians, etc. Insome embodiments of the invention, the profession information is used toestimate a contact quality score, for example, doctors being morecareful with PPE than teachers. It is a particular feature of someembodiments of the invention, that differences within such a group, suchas between different doctors, are determined. In some embodiments of theinvention, a subject's score is modified according to the profession,for example, to compensate for criticality of the subject and/or to lackof control of the subject (e.g., a bus driver) over number of contacts.

In some embodiments of the invention, a subject provides professioninformation or other information used to adjust scoring by scanning abarcode (or other machine-readable item such as a barcode or RFID chipidentity card) which is optionally digitally signed with suchinformation. Optionally, this allows the device to know the professioninformation, but may not allow the device and/or the informationprovider to link the request for data to a particular individual. Thuspotentially maintaining privacy.

Characteristics of Population Potentially to Meet

In some embodiments, the kind of population that a certain subject canpotentially meet will either increase or decrease the score. Forexample, teachers that meet many children will be provided with a higherscore, since if and once the children are infected by the teacher, thechildren return home and potentially infect their families. While forexample, a doctor that works at a prison would potentially receive alower score since the incarcerated people in the prison are not leavingand probably will not infect anyone else (the infection is contained tothe prison alone).

Another example, if a certain subject meets only a certain number ofindividuals, and mainly only those individuals, for example a subject ina close community, then that subject will receive a lower score.

Characteristics of Population that a Subject Actually Met

In some embodiments, if a certain subject meets people that wereidentified as superspreaders, this will influence the score byincreasing their score, also when compared to subjects that do not meetsuperspreaders and/or regular people. In some embodiments, theinformation regarding meeting a superspreader is performed between themobile devices in an anonymous matter, for example, as will be furtherexplained below.

The Nature of the Locations

In some embodiments, the nature of a location means if it is in a closedplace, if it is in an open space, if it is indoors, if it is outdoors,quality of ventilation or any combination thereof. In some embodiments,the nature of the locations can drastically change the score given to asubject. It has been shown that a likelihood of a subject transmitting apathogen increases by a factor of between about 10 times to about 100times when the location is indoors and/or in a closed space. This isbecause the risk of infection is increased due to the possible buildupof the airborne pathogen-carrying droplets, the pathogen likely higherstability in indoor air, and/or a larger density of people.

In some embodiments, if the location is indoors or in a closed location,then the score given to the subject for a contact will increase.

In some embodiments, other factors that influence the increment orreduction of the likelihood of a subject transmitting a pathogen indoorsare one or more of ventilation rate, use of natural ventilation,avoidance of air recirculation and use of air filters.

In some embodiments, the system will comprise information on indoorlocations related to the ventilation rate, use of natural ventilation,avoidance of air recirculation and use of air filters. In someembodiments, an indoor place comprising a high ventilation rate scorewill provide a lower score to the individual when compared to a placehaving a low ventilation rate score.

The Kind of Places Usually Visited by the Subject

In some embodiments, subjects that are prone to frequent religious orsecular events, like in a synagogue, a church or a mosque or a dancingvenue, where the people are in close proximity to each other, and talk,pray, sing and/or breathe deeply and/or mingle more, will receive ahigher score (e.g., for such a contact event) than those who do notfrequent religious events. In some embodiments, similarly to above, alsosubject that are prone to frequent sports events will receive a higherscore. In some embodiments, places that are frequented regularly by alarge quantity of individuals (including public transportation,detectable for example, by geolocation and/or regular start-stopmovement that matches a public transportation profile and/or base donpayment activity using the tracking electronic device) will be marked aspoints on interest for the potential spreading of the infectiousdisease/virus/pathogen, and subjects that frequent those places willreceive a higher score.

The Length of Time at the Locations

In some embodiments, the length that a subject stays in one place willcontribute to the determination of the probability to infect othersand/or to be infected by others. For example, a subject that visits manyplaces but stays there just for a minute or two might receive a lowerscore (e.g., for a contact event) than a person that stays for longer ina few places, since staying longer at one place potentially increasesthe chances to infect and/or be infected.

Historical Geolocation Data of the Individual

In some embodiments, historical data of the location of an individual isused to assess the potential geolocation activity of that specificindividual. For example, Google Maps® data saved in servers, Waze® datasaved in servers, and other geolocation applications configured to savegeolocation activity data. In some embodiments, individuals having ahigh volume of movement data (and/or high usage of publictransportation) in their historical geolocation data will receive a highscore. In some embodiments, the historical data is used to furtherassess a reliability of change in behavior of a subject, for example todetermine if to increase score in cases where the actual geolocationdata changes drastically (for example if there is a risk that a subjectwants a higher score to receive the vaccine and increases his movementsto achieve so).

Actual Geolocation Data of the Individual

In some embodiments, actual measured geolocation data of each individualis monitored to assess their potential to meet other people. In someembodiments, people which show high number movements during the day inareas where other people are located will receive a high score. In someembodiments, actual geolocation data of each individual is monitoredusing one or more of:

1. Electronic devices, for example the location provided by the GPS oftheir own cellphones;2. Using face recognition technology based on one or more of: a) videosurveillance data received from available sources, for example streetcameras, ATM's, private surveillance cameras in stores, buildings andhouses, etc.; b) social media.3. Digital activity, for example credit card usage, IP address usedwhile using a computer or an electronic device, antennas that receivedata while performing a phone call.

Optionally or additionally, such actual geolocation data is used insteadof or in addition to actually identifying contact between people.

Historical Medical Data of the Individual

In some embodiments, historical medical data of each individual isassessed to provide a score. For example, as mentioned above,individuals with chronic coughing will receive a high score since theyhave potentially a higher chance to transmit the infectiousdisease/virus/pathogen. In some embodiments, individuals having abackground condition that enhances the chances of transmitting thedisease will receive a high score.

Actual Medical Data of the Individual

In some embodiments, during the pandemic, every new medical dataconcerning each individual is monitored to assess if the new dataindicates a change in the medical status of the individual regardingtheir potential to infect others. Using the example above, if a personis diagnosed with chronic coughing it will increase their score (e.g.,in general and/or per contact).

Third Party Information Regarding the Individual

In some embodiments, third party information from individuals informingon others will be assessed to decide if the information needs to affectthe score. For example, if a third party informs that a person thatshowed low movement data and received a low score is actually performingmany movements, once the information is verified, the score will changeaccordingly. The contrary is also valid, for example, a third partyinformed that a person that showed high movement data and received ahigh score is actually staying at home, once the information isverified, the score may change accordingly.

Dedicated Mandatory App

In some embodiments, in view of the pandemic, the government may orderthe citizens to install a dedicated application on their smartphones (orother smart devices like tablets, smart watches, smart glasses, etc.) tohelp the government with the logistics of the vaccination procedures. Insome embodiments, the government (or other body) provides the publicwith such dedicated smart devices. In some embodiments, the app and/orthe smart device is configured to inform on the user's location at alltimes and to communicate with adjacent smart devices (via Bluetooth forexample) to assess the interactions between users, for example vicinitybetween users, movement of users, etc.). In some embodiments of theinvention, already existing software may be used, for example, bothandroid and is based cellphones have software (e.g., as an operatingsystem service) which can detect proximity of others and such softwaremay be used or improved to provide functionality as described herein.

In some embodiments, such app can be used to provide informationregarding how many unique people the user meets. For example, a certainuser can meet many people but they are all the same people all the time.While another user can meet fewer people but each one is a differentindividual. In some embodiments, the second user may potentially receivea higher score and therefore receive treatment first. In someembodiments, such app and/or smart devices are also used to assess theprogression of the vaccination procedures and the efficacy of thevaccination procedure. In some embodiments, individual data arrivingfrom each user is coupled with their health information (sick,vaccinated, recovered, etc.) to further assess the progression of thevaccination procedures and the efficacy of the vaccination procedure.Optionally, if the persons met by a user are vaccinated or otherwisedetermined to be immune, such contacts may not count and/or be weightedlower.

In some embodiments, the app will be also used to send personalizedcommunication to the users, for example, to come and be vaccinated. Insome embodiments, in view of the information received from the app,specific actions are taken, for example, send a communication to theuser to enhance his awareness to behavioral rules during pandemic, tocome and be vaccinated, to avoid certain locations, which are at highrisk of contagion.

Dedicated Voluntary App

In some embodiments, in view of the pandemic, the population isencouraged to install a dedicated app, where those that do install theapp are rewarded. In some embodiments, the reward is priority to receivetreatment.

Monitoring Behavior of Subject

In some embodiments, the behavior of the subject is monitored inrelation to safety features performed by the subject, for example,wearing a mask (e.g., analyzing images taken during calls or otherlooking at screen of cellphone), washing his hands (e.g., analyzingsounds of water running or movement by a smartwatch), keeping socialdistancing (e.g., based on Bluetooth power levels and/or NFC detection),moving between multiple locations, etc. In some embodiments, these aremonitored using the same devices/methods as disclosed above.

Exemplary Scoring Method

In some embodiments, each individual in a population (e.g., above 100,1000, 10000 and/or 100000 individuals) is provided with a score definingthe potential level of superspreading of each individual. In someembodiments, scores are defined as number of contacts (see herein), andthe number of contacts that are counted are from about 10 to about 100,optionally from about 100 to about 1000, optionally from about 1000 toabout 10000, for example 100, 400, 1000, 2000, 10000 or intermediate orgreater numbers. In some embodiments, a high score defines a highpotential of superspreading, while a low score defines a low potentialof superspreading. In order to facilitate the explanations of theinvention, a scoring scale from 0 to 100 will be used. It should beunderstood that other scales can be used, like heat-map scoring, decimalorder scales, etc., all of which are included in the scope of theinvention. In some embodiments of the invention, the score is openended. In some embodiments of the invention, the score is normalized,for example, to other scores. The normalization need not be linear. Insome embodiments of the invention, the score is a scalar. In someembodiments of the invention, the score is multi-dimensional, forexample, including a superspreader potential dimension and a variabilityin behavior dimension)

In some embodiments, the score is calculated using weighted scoringmodels, in which one or more factors and/or components are assessedaccording to the received information data. Referring now to FIG. 3,showing a schematic flowchart of a method of calculating a weightedscore, according to some embodiments of the invention. In someembodiments, the system receives information data about a subject 302.In some embodiments, the information data is divided according to thesource of the information data 304, for example, electronic information306 from smartphones, cameras, credit card information, etc.,geographical information 308, for example from GPS or cell towers,governmental information 310, for example from the census bureau or EMR(electronic medical records), human information 312, for example fromother individuals calling an providing the information about otherindividuals, and one or more of the factors and/or components disclosedabove. In some embodiments, the system then calculates a weighted scoreof each information, optionally according to a predetermined criterion314. In some embodiments, the system then generates a total score fromthe different weighted scores, optionally according to a predeterminedcriterion 316. In some embodiments, the system then provides a listcomprising an order of treatment, which is then used to actually treatthe population 318.

In some embodiments, the score comprises a plurality of components, forexample predicted likelihood of a subject transmitting an infectiousdisease/virus/pathogen, predicted likelihood of a subject contracting aninfectious disease/virus/pathogen, relative health risk to a subject ifsaid subject contracts a infectious disease/virus/pathogen, damage tosociety if the subject contracts a infectious disease/virus/pathogen;one or more of the above optionally in view of physical proximity datato other subjects.

In some embodiments, physical proximity data of a subject with othersubjects is calculated by including one or more of:

1. The number of subjects the subject potentially is in contact with;

2. The potential and/or actual distance of the subject to the othersubjects;

3. The time length of the potential and/or actual encounter of thesubject with the other subjects.

In some embodiments of the invention, the score is updated for and/orafter each contact event. In some embodiments of the invention, updateis at end of the day, which may allow aggregating multiple meetings witha same person. Optionally or additionally, the score is updated per aset of contact events. In some embodiments of the invention, the scoreis calculated after all contact events are collected, for example, basedon an analysis of a contact-network to identify individuals, which, ifvaccinated, will best stop infection. Such analysis may be carried outby simulating the contact network and trying out various vaccinationschemes and/or removal of various individuals and/or sets ofindividuals.

From Score to Treatment

In some embodiments, once the scoring of each individual is achieved, oroptionally the scoring of a high number of individuals of thepopulation, a list is created having the order in which each individualwill receive the treatment. In some embodiments, the list is optionallydivided by groups, for example, all the individuals that scored between100 and 90 are grouped in group A, which will receive first thetreatments. Then all the individuals that scored between 90 and 80 aregrouped in group B, which will receive second the treatments, and so on.

Informing the Public

In some embodiments, once the list is made, individuals will be informedon when and where to go and receive the treatments, for example, bymeans of emails, dedicated apps in their cellphones, over the media,etc.

Exemplary Simulations

In some embodiments, models and simulations are run in dedicatedcomputers, for example, to assess the potential progression of thetreatments and the probable time to reach herd immunity and/or selectvalues for various parameters. In some embodiments, simulations includethe insertion of one or more of actual data received from individuals,simulated data of/from individuals (in case is necessary to run probablescenarios). In some embodiments, evaluations and models utilize one ormore of neural networks, machine learning and dedicated simulations.

In some embodiments, the simulations take under consideration and modelthe probability of the treatments to work (or not work) on theindividual.

In some embodiments, the simulations take under consideration and modelthe kind of population that a certain subject can potentially meet andthe potential population those individuals will potentially meetafterwards. For example, teachers that meet many children will beprovided with a higher simulated score, since if and once the childrenare infected by the teacher, the children return home and potentiallyinfect their families. While for example, a doctor that works at aprison would potentially receive a lower simulated score since theincarcerated people in the prison are not leaving and probably will notinfect anyone else (the infection is contained to the prison alone).

In some embodiments, simulations are performed to evaluate parametervalues used to identify a superspreader and possibly how todifferentiate them from regular individuals.

Exemplary Spreading Network

In some embodiments, before, during and/or receiving the informationregarding the individuals in the whole population, a network 400 of thepopulation is created, as shown for example in FIG. 4. In someembodiments, the network is constantly updated by the system. In someembodiments, the network is used to determine the potential spreading ofthe infectious disease/virus/pathogen if a certain individual isinfected. In some embodiments, when possible, clusters in the networkare identified, for example clusters 402 through 412 in network 400. Insome embodiments, when evaluating whom to provide treatments, the systemassesses the individuals in the clusters and performs analysis andsimulations to choose which individuals to treat (e.g., individuals thatinterconnect clusters). In some embodiments, this is performed inaddition to the scoring performed and generated on each singleindividual. For example, it can be seen that individual 414 belongs toboth clusters 402 and 404, thereby creating a potential bottleneck (orbridge) between clusters. Therefore, it would be advantageous to treatindividual 414 to protect cluster 404 from potential infections comingfrom cluster 402. Same logic is applied to individual 416. Treatingindividual 416 can potentially protect clusters 410 and 412 frompotential infections coming from cluster 402. In some embodiments, thesystem identifies potential key individuals and/or potential key groupsof individuals to treat first in order to potentially protect clustersof individuals. In some embodiments, the systems performs thisassessment in view of the number of doses available to the population.For example, if there is a large number of doses, instead of treatingthe individuals located in the bottlenecks, it might be advantageous totreat first all individuals in the cluster 402, thereby potentiallyprotecting the rest of the clusters from infection.

In one example, the system selectively removes individuals to identifywhich set of N individuals (e.g., where N is the number of doses to beused) is best to remove. This can be done using brute force approaches,e.g., of trying a plurality of sets. Optionally or additionally, this isdone by selecting sets of individuals (e.g., based on some sharedcharacteristic, such as profession or place in the network) and seeingthe effect of vaccinating these individuals. Optionally or additionally,a different search technique is used, e.g., treating the problem as anoptimization problem.

Exemplary Use of the System and Methods for Testing

In some embodiments, the system and methods are used to identifyselected subjects to be tested for the disease. In some embodiments, thetesting is used to assess one or more of the progress of the disease,the progress of the treatments, the progress of the herd immunity, etc.

Exemplary Use of the System and Methods for Determining Who Will Receivea Certain Type of Vaccination

In some embodiments, during the development of vaccines for a certaindisease, different vaccines comprising different vaccine potencies aredeveloped. In some embodiments, vaccine potency is a quantitativemeasure of the specific ability of the vaccine product to achieve anintended biological effect defined in a suitable biological assay basedon the attribute of the product that is linked to the relevantbiological properties. In some embodiments, the system is used toidentify which individuals will receive which types of vaccines inrelation to their potency. For example, individuals that received and/orwere identified as a high superspreading score by the system would bevaccinated with more potent vaccines, when compared with otherindividuals having lower superspreading scores. In some embodiments,those individuals having lower superspreading scores might eitherreceive later a vaccination or receive a vaccine having a lower potency.

Exemplary Privacy Settings

In some embodiments, the system comprises one or more layers ofprotection and/or privacy. In some embodiments, layers of protectioninclude one or more of encryption algorithms and/or software.

For example, encryption algorithms and/or software convert the data intociphertext to transform the original data to a non-readable formataccessible only to authorized parties who can decrypt the data back to areadable format. The process of encrypting and decrypting messagesoptionally involves keys. The two main types of keys in cryptographicsystems are symmetric-key and public-key (also known as asymmetric-key).

Exemplary types of keys: Symmetric-keys: In symmetric-key schemes, theencryption and decryption keys are the same. Communicating parties musthave the same key in order to achieve secure communication. Public Keys:In public-key encryption schemes, the encryption key is published foranyone to use and encrypt messages. However, only the receiving partyhas access to the decryption key that enables messages to be read. Insome embodiments, the length of the encryption key used in the system isone or more of 128-bits, 256-bits, 1024-bits and 2048-bits.

In some embodiments, the privacy of the users that information is beingcollected is protected by anonymizing the user at the source. Forexample, when a cellular phone/electronic device is used to collect therelevant data, the name of the owner of the electronic device is eitherencrypted and/or anonymized so any interaction with external sources(for example the servers of the systems) will be managed without the useof the actual name of the user but using an encrypted and/or anonymizeduser name. In a practical example, electronic devices/cellphones areused to evaluate, quantify and qualify the interactions of the user withother people during the day. In some embodiments, the cellphonecommunicates with other cellphones to monitor the interactions(distance, location, duration, etc.). In some embodiments, whencollecting the data about the interactions, the software in theelectronic device will use encrypted and/or anonymized user names. Forexample, using the names as mentioned in the example below, John Doe,Jane smith and Mark Lite are three users, all having cellphones andoptionally comprising a dedicated app for this purpose. In someembodiments, the software of the app in the electronic device willencrypt and/or anonymize the names to be, for example, John Doe=user265498756124565526, Jane smith=user 31678465923128 and Mark Lite=user463212887036554. From this point on, all communications between theirelectronic devices and external sources will be performed using theencrypted and/or anonymized user names. Optionally, for example asdescribed below, the user IDs or what is exchanged between telephones)are non-unique. For example, provided at a ratio of, for example 1:100,1:1000, 1:10000, 1:100000 between codes and individuals. While this maymean a potential for confusion between individuals, such confusion maybe small, while the increase in difficulty of identifying a use based onthe tracked information can significantly increase.

Furthermore, when assessing the order of receiving treatment, eitherindividually or by groups, (e.g., at a server) may comprise theparameters needed to enter a certain group (for example, the first groupto receive treatment, the second group to receive treatment, etc.). Insome embodiments, the action of comparing between the parameters of eachgroup and the collected data from the user will be performed inside andby the electronic device itself, thereby avoiding sending data to theservers. In some embodiments, the electronic device will contact theserver to requests the parameters, the electronic device will performthe necessary calculations and will generate a score that will be sentback to the server in an anonymized matter (as explained before). Insome embodiments, additional information regarding each individual user,as disclosed above, is also downloaded to the electronic device for useof calculations. Once the calculations are finished, the resulting datawill be sent to the servers and, in response, the server will optionallysend a notification to the user to go and receive treatment.

It is a particular feature of some embodiments of the invention thatinformation about a person's activities, locations, meetings, are notsent out of the device except as, for example, an overall score or apriority for treatment. In some cases, the behavior is sent out but isanonymized and/or condensed, for example, indicating a number (e.g.,optionally not an exact number and/or time and/or date) of people metand a number of large congregations attended (optionally not an exactnumber and/or location), but with enough details removed so thatidentification of an identity of the device owner will be difficult orimpossible.

In some embodiments, whether the calculations are performed on theservers or on the electronic device, the encryption and/or anonymizingof the name of the user is always used. In some embodiments, the meansto read between the encrypted/anonymized user name and the actual namewill only be available in the user's electronic device.

In some embodiments, the notification for getting treatment may or maynot contain information regarding the results of the calculations. Forexample, an individual that was identified as a superspreader may or maynot receive information about the fact that he/she was identified assuch. In some embodiments, the potential advantage of not providing suchinformation is to further enhance the privacy protection of the user.For example, an onlooker may not be able to tell if a user received ahigh score due to his own behavior, the behavior of those he meetsand/or an underlying health condition, which may put them at higherrisk.

In some embodiments, dedicated codes, for example in the form ofcoupons, will be provided to individuals having important/relevantprofessions (like doctors, police, etc.). In some embodiments, insertionof the codes into their personal electronic devices will inform thesystem that that encrypted/anonymized user needs a correction in theirscore. In some embodiments, the correction can be either increasing thescore or decreasing the score. In some embodiments, when the electronicdevice detects certain behavior, like an increase in the movements ofthe user, the electronic device (for example via the dedicated app) willwarn the user that his score will be changed if the behavior is notchanged. In some embodiments, changing the score can be eitherincreasing or decreasing the score.

Exemplary Methods for Identifying Superspreaders with High Levels ofAnonymization

It has been shown that individuals are concerned that the authoritiesand/or companies are constantly collecting data with or without theirconsent for a plurality of reasons. It is also scope of some embodimentsof the invention to provide a method of identifying superspreaderswithout the need to collect data that could potentially be used to leadto the identification of the person in question.

As an example, consider three types of systems having different levelsof possible anonymization techniques, in accordance with variousexemplary embodiments of the invention:

1. A system that uses personal information but does not transmits thatpersonal information about the individual;

2. A system that uses personal information but does not transmitsspecific information that could be used to potentially identify theindividual; and

3. A system that does not require any personal information to work.

In some embodiments, the anonymization techniques described in the“Exemplary Privacy Settings” section belong to the first type and/or thesecond type of technique, where relevant data (positional data, personaldata, etc.) is used by the system but: a) anything that is transmittedis either coded and/or anonymized when used, or b) the necessarycalculations are performed on the electronic device itself, therebyavoiding sending any personal data at all.

In the following paragraphs, systems belonging to the third type ofsystem comprising a method of identifying a superspreader thatpotentially does not require the use of any personal information will beexplained.

Exemplary “ID” Based System for the Identification of Superspreaders

In some embodiments, the system is based on the followingassumptions: 1) all individuals comprise an electronic device of anykind; 2) on each electronic device there is installed a dedicatedapplication/app that runs the system's software (as will be explained inthe following paragraphs); and 3) when individuals meet otherindividuals, information is passed between their electronic devices.

Referring to FIGS. 5a-f , showing flowcharts of exemplary methods ofidentification of superspreaders, with an anonymization, according tosome embodiments of the invention. In some embodiments, the methodbegins when a user downloads the software, in the form of an application(or app) into their electronic device 502. In some embodiments,dedicated electronic devices comprising the software will be distributedto those individuals who either do not possess an electronic device ordo not want the software downloaded into their electronic devices. Insome cases, the device has such software preinstalled thereon.

In some embodiments, when the individual opens the application,optionally, the individual will be requested to provide and/or insert anidentification (ID), optionally using alphanumeric digits 504,optionally comprising a high number of digits, for example 10 digits, 20digits, 40 digits. In some embodiments, the system will automaticallyprovide an ID to the device (e.g., will be generated locally, forexample, as a random number or as an encrypted version of the user ID.To facilitate the explanations below, a 20 digits ID will be assumed. Itshould be understood that other length of ID can be used, noting thedifference between IDs that are expected unique and IDs that are notexpected to be unique and within unique IDs, IDs that also a particularpart thereof is long enough to be expected to be unique.

At this point, all users have an electronic device with a software inthe form and/or as part of an application in which an ID comprising 20digits has been assigned to the device. It should be noted that the useof “application”, “app” and “software” are interchangeable for theexplanation of the following methods. From here, four different methodscan be used, as will be further explained bellow.

Anonymized Method 1—Count

Referring to FIG. 5b , showing a flowchart of exemplary anonymizedmethod 1, according to some embodiments of the invention. Following theletter “A” from FIG. 5a to FIG. 5b , in some embodiments, when anelectronic device comes in proximity to another electronic device, thedevices exchange full IDs 506 between each other, and the software savesthe received ID in the application itself. In some embodiments, after acertain period of time, for example, after one day, after 7 days, after14 days, after 30 days, or intermediate or shorter times and/or onrequest by a central server, the application analyzes the IDs stored inthe electronic device 508. In some embodiments, analyzing comprises oneor more of counting the number of IDs that were received, the number oftimes that a specific ID was received and the number of IDs received ina day. In some embodiments of the invention, the counting is weighted sodifferent IDs get a different weight, for example, IDs with a high scoremay be weighted higher, for example as described herein. In particular,IDs that are associated with contacting other suspected superspreadersmay receive a higher score. In some embodiments, the software thengenerates a score based on the result of the analysis.

At this point one of two different methods is optionally applied, acompletely anonymous method and a semi-anonymous method 510.

In some embodiments, when the method is a completely anonymous method,the method continues following the letter “E” back to FIG. 5 a.

In some embodiments, the application receives from the server a scale ofscores 512. For example, continuing using the scale as above, from 1 to100, group 1 are those individuals having a score higher than 90, group2 are those individuals having a score from 80 to 90, and so on. In someembodiments, the software then compares the score generated from theanalysis with the scale of scores 514. In some embodiments, based on theresult of the comparison, the software provides the user of the devicewith relevant information related the treatment to be received. Forexample, a predetermined date to receive vaccination (informationreceived with the scale of scores from the server) and/or the groupnumber for receiving the vaccination. In some embodiments of theinvention, the scale of scores is generated by the receiving informationabout the score distribution and selecting cutoff values optionallybased on available vaccines. Optionally, the information comprisesreceiving scores form some or all devices. Optionally, only astatistical same of scores is used, for example, fewer than 10%, 1%,0.1% of available devices, for example, between 50 and 10,000 scores. Itis noted that such scores may be delivered anonymously, for example,using an anonymous web service, optionally anonymized using anonymitytools such as Tor, so that the deliverer of each score is unknown.Optionally, the scores are digitally signed by the sender.

Returning to FIG. 5b , in some embodiments, when the method is not acompletely anonymous method, the method continues following the letter“F” to FIG. 5 f.

In some embodiments, after the software has generated a score based onthe analysis, the software sends the score, together with the full ID(here and in other examples, a full ID may be encrypted or Hashed orotherwise used to generate a token, which, optionally, is notdecipherable by the server), to the server to be used to evaluate ifthat specific individual is potentially a superspreader or not, whencompared to other users 518. In some embodiments, the server performs anevaluation by comparing the scores of the different IDs 520 andgenerates a treatment list according to the result of the evaluation. Insome embodiments, the server then sends back notification regarding thevaccination procedures 522, for example, when to go to receive avaccination, the group number, etc.

In some embodiments, optionally, the user can choose to respond to aseries of personal questions presented by the application, which arethen translated into factors that affect the score, for example asdisclosed herein.

In some embodiments, the user choses the level of anonymity that thesystem will work (completely anonymous or partially anonymous), e.g.,different individuals may have different anonymity levels in a samevaccination prioritization system.

Anonymized Method 2—Count with Transmission of Partial Username

Referring to FIG. 5c , showing a flowchart of exemplary anonymizedmethod 2, according to some embodiments of the invention. Following theletter “B” from FIG. 5a to FIG. 5c , in some embodiments, when anelectronic device comes in proximity to another electronic device, thedevices exchange partial IDs, for example only the last 10 digits of the20 digits of the ID 524, and the application saves the received partialID in the application itself. In some embodiments, the partial ID is asubstantially unique partial ID. For example, the use of the last 10digits of the 20 digits increases the chances that the partial ID is asubstantially unique partial ID. In some embodiments, the partial ID isa substantially non-unique partial ID. For example, the use of the last3 digits of the 20 digits increases the chances that the partial ID is asubstantially non-unique partial ID, since there is an increased chancethat the same last 3 digits appear in more than one ID. It should beunderstood that the word “substantially” in this context does not meanto be vague, but it is related to the statistical probabilities that apresented partial ID could be identical to another.

In some embodiments, a potential advantage of exchanging only partialIDs is that it decreases the chances that the specific individual couldbe identified. It is also noted that, in some embodiments, transmittingpartial ID might introduce errors to the analysis of the meeting betweenindividuals since it increases the possibility that one or moreindividuals will transmit the same partial ID. Since the scope of themethod is to protect the privacy of the individuals while contemporarilyproviding an indication of a potential superspreader, a certain marginof error is acceptable.

In some embodiments, when a received partial ID is stored in theapplication, it is stored (or only transmitted that way) by adding itsown partial ID. In some embodiments, a potential advantage of using thismethod is that if such pairs of partial ids are transmitted to a thirdparty, such third party can track and count unique meetings.

In some embodiments, after a certain period of time, for example, after7 days, after 14 days, after 30 days (or other times for as discussed inthe previous method), the application analyzes the partial IDs stored inthe electronic device 524. In some embodiments, analyzing comprises oneor more of counting the number of partial IDs that were received, thenumber of times that a specific partial ID was received and the numberof partial IDs received in a day. In some embodiments, the software thengenerates a score based on the result of the analysis. In someembodiments of the invention, a repeat meeting with a same partial ID isnot counted or given a lower weight. Other methods of counting asdescribed herein may be used. In some embodiments of the invention, thecount is otherwise normalized. For example, the distribution of countsmay be used to reconstruct an estimate of actual diversity of meetings,using statistical methods of distribution estimation, such as known inthe art. Such methods may also be used if instead of always transmittingthe ID the ID is only sometimes transmitted. This statisticaldistribution may be used to estimate the percentage of unique meetingsvs percentage of repeat meetings, for example, assuming a givendistribution shape for repeat meetings. Such a given shape may beprovided, for example, by a central server (e.g., based on real-timedata collection) or a priori. Optionally or additionally, suchdistribution may be created by sometimes applying method 1 of full IDtransmission.

At this point, one of two different methods is optionally applied, acompletely anonymous method and a semi-anonymous method 528. In someembodiments, when the method is a completely anonymous method, themethod continues following the letter “E” back to FIG. 5 a.

In some embodiments, when the method is not a completely anonymousmethod, the method continues following the letter “F” to FIG. 5f . Thesealternatives may be applied as above.

Anonymized Method 3—Count with Transmission of Partial Username andUsername Changes Periodically

In this method, which can be used as a variant of the last two methods,and is shown in FIG. 5d , the ID or partial ID used by the device ismodified.

In some embodiments, for example, after the certain period of timementioned above for counting, the partial ID that is used for thetransmission of IDs between is changed by the system and/or theindividual itself 534. The actual ID may be changed or a different partof the ID transmitted. In some embodiments of the invention, theoriginal ID is used as a seed to generate a series of pseudo random IDsto be used for transmission. In some embodiments, for example, when thesystem changes the transmitted partial ID, the system transmits insteadof the last 10 digits of the ID, the first 10 digits of the ID; or forexample the first 5 digits together with the last 5 digits. It should beunderstood that the above-mentioned are only examples, and that othermethods of randomizing the partial ID that is transmitted are alsoincluded in the scope of some embodiments of the invention. In someembodiments, periodically changing the partial ID may further cause toerrors since it further increases the possibility that one or moreindividuals will transmit the same partial ID. As mentioned above, afurther certain margin of error is still acceptable.

The method then continues with various options for acting on the score,for example, a completely anonymous method and a semi-anonymous method536. In some embodiments, when the method is a completely anonymousmethod, the method continues following the letter “E” back to FIG. 5 a.

In some embodiments, when the method is not a completely anonymousmethod, the method continues following the letter “F” to FIG. 5 f.

In this and other embodiments it is noted that other follow upactivities may be provided in addition or instead, in particular,activity by a central server may be reduced. For example, a user maysimply go to a vaccinating station and show their score and be given avaccination or date therefore accordingly.Anonymized Method 4—Complex Count with Transmission of Partial Username,at Least One Additional Number and Optionally Username ChangesPeriodically

Referring to FIG. 5e , showing a flowchart of exemplary anonymizedmethod 4, according to some embodiments of the invention. In someembodiments, a complex count method is used for probabilisticallydetermining if a certain individual is a potential superspreader. Insome embodiments, the complex count method comprises the use of twoindependent counts for the determination.

Following the letter “D” from FIG. 5a to FIG. 5e , in some embodiments,when an electronic device is in proximity to another electronic device,the system is configured to exchange not one, but at least two IDnumbers as following.

In some embodiments, the first number to be exchanged is the partial ID538. In some embodiments, the exchange of the first number is asdisclosed in method 1, where the full ID is exchanged. In someembodiments, the exchange of the first number is as disclosed in eithermethod 2 or method 3, where a partial ID is exchanged. For theexplanation of the method and as disclosed in FIG. 5e , the explanationwill refer to the transmission of a partial ID. It should be understoodthat this method could also be applied when transmitting the full ID.

In some embodiments, the first number is used to evaluate the number ofcontacts.

In some embodiments, the second number to be exchanged is a differentset of digits, either created by the system or inserted by the useritself 538. In some embodiments, the actual second number to beexchanged is a partial second number, similar to what is done with thefirst number.

In some embodiments, the second number is used to evaluate if theindividual is meeting people from outside a limited subpopulation and/ortrack the general promiscuousness (optionally in a non-sexual sense) ofsuch individuals.

In some embodiments, contrary to the first number that always isexchanged in an encounter, the second number is exchanged at a certain“rate of probability”. In some embodiments, a rate of probability is,for example, a calculated number that responds to the question: what isthe percentage rate necessary to separate between a superspreader and anon-superspreader. In some embodiments, the rate of probability isachieved by running a simulation, and checking for different probabilityrates the degree of discrimination. For example, a rate of probabilitycan be 3%, 5%, 10%, 20% or smaller or intermediate values. In someembodiments, this means that, if the rate of probability is 3% forexample, an electronic device that encounters 100 electronic deviceswill exchange 100 times (100% of the times) the first number and 3 times(3% of the times), in addition to the first number, will also exchangethe second number. In some embodiments, the rate of probability is lowerthan 100.

In some embodiments, from the moment the system is activated, theelectronic devices of the individuals will begin collecting first andsecond numbers as long as they continue to meet other electronicdevices.

In some embodiments, when a certain electronic device exchanges thesecond number (under the rate of probability), the electronic devicewill exchange in addition to its second number, all second numbers thatwere collected until that moment. In some embodiments, potentially andprobabilistically, an individual that is a superspreader will collect ahigh number of second numbers because he/she meets many differentindividuals, who themselves meet different individuals. While anindividual “trapped” in a subpopulation may only collect at most as manynumbers are there are persons in the subpopulation. Therefore, in someembodiments, when someone meets that superspreader, many second numberswill be potentially exchanged from that superspreader to that someone.In some embodiments, those second numbers collected from otherindividuals will later be used to indicate a specific meeting between anindividual and a superspreader.

In some embodiments, an individual that collects many second numbers,potentially and probabilistically, met a superspreader and/or is onethemselves. In some embodiments, this information is used to cause aneffect (e.g., increase) in the scoring of the individual and/or in theweight of the contact.

The collected IDs may be counted after a time, e.g., as described in theother methods (540) In some embodiments, optionally, after the certainperiod of time mentioned above, the partial ID transmitted betweendevices is changed by the system and/or the individual itself 542 asdisclosed above.

Optionally, a method of follow-up is selected, for example, a completelyanonymous method and a semi-anonymous method 544. In some embodiments,when the method is a completely anonymous method, the method continuesfollowing the letter “E” back to FIG. 5 a.

In some embodiments, when the method is not a completely anonymousmethod, the method continues following the letter “F” to FIG. 5f , forexample as described above.

In any of the above methods, optionally, statistical information aboutcollected first and/or second numbers (e.g., how many people had howmany collected first and/or second numbers) may be transmitted to theserver to help generate a better picture of these statistics of thepopulation's collected information. In some embodiments of theinvention, more than one second number is used.

Optionally, each additional such number is transmitted at a differentprobability. This allows different numbers to give information aboutdifferent characteristics of subpopulations. It is noted that if onlyone number is used and its transmission rate not selected correctly, itmay result is propagation of such second number over a significant partof the network of contacts, making it less useful for identifying moreclosed and more open parts of the network.

In some embodiments of the invention, no additional second number isused. Rather the first number is optionally counted and/or transmittedusing such probabilistic transmission rate. So, for example, during acontact, the second device will store the received ID of the firstcontact in a memory for storing and/or counting contacts with a first IDand also, with some probability store that number in a second memoryused for counting and/or tracking second numbers. Additional memoriesmay be provided if more numbers are tracked.

In some embodiments of the invention, a relatively small non-unique IDis used and this ID may be used as an index for the first and/or secondmemory. For example, when meeting an individual who passes a non-uniqueID 234, memory location 234 is increment (optionally in a weightedmanner). If a second ID list, say (123, 456, 789) is passed, the countin each of those indexes in the second memory is incremented (optionallyin a weighted manner). In some embodiments, only one bit (or anequivalent thereof) is saved for each ID in the second memory and it iseither set or unset. Optionally, the second ID uses more bits than thefirst ID, for example, 2, 3, 4, 5 times as many bits or an intermediateof smaller or greater number. This may allow preventing saturation ofsecond ID tracking. Optionally or additionally, a statistical estimationof the actual number of second IDs is reconstructed using statisticalmethods and the number of second IDs received and optionally a count ofat least a sample thereof. Optionally, an assumption is made about theexpected shape of distribution of second IDs.

Optionally or additionally, the number of second IDs collected istracked as a function of time. Optionally, potential superspreaders (andwhich get an increased score and/or contact weight) are those who earlyon accumulate a larger number of second IDs (e.g., as compared to otherpersons an individual comes in contact with) and/or those persons (e.g.,with repeated contact) whose second ID count asymptotes later or not atall.

Regarding repeat meetings with an individual, it is noted that anindividual is a sum of all his contacts, so that after a time, if and asthat individual meets new contacts, the individual changes and should beweighted more heavily. Such tracking can be by time and/or can be bychange in count of first and/or second IDs that an individual has, whichcount (and/or a date of contact) is optionally transmitted upon meetingand may be stored.

Exemplary Effect of Meeting an Individual that has Met PotentialSuperspreaders

Referring now to FIG. 6, showing a schematic flowchart of an example ofthe effect caused when a certain individual meets another individualthat had been in contact with possible superspreaders, according to someembodiments of the invention. In some embodiments, as previouslymentioned, when a Device A meets Device B 602, IDs are exchanged andoptionally also information regarding previous meetings 604. In someembodiments, for example, the software in Device A, that has justreceived the ID and previous meetings of Device B, will evaluate thereceived data 606. In some embodiments, evaluation of data comprises oneor more of evaluating the number of meetings Device B has had 608 andthe kind of individuals were met during those meetings 610. In someembodiments, since these operations were also previously performed byDevice B during its meetings, the information about the possible meetingwith a potential superspreader will be also delivered by Device B toDevice A, when information is exchanged. In some embodiments, thesoftware in Device A will generate a score to the meeting between DeviceA and Device B, also in view of the information regarding the kind ofindividuals that Device B has met 612. In some embodiments, the score isthen saved in Device A 614 to be used in the final score calculations,as previously described.

Exemplary Methods

In some embodiments, an exemplary method of providing the order oftreatments to a population comprises:

1. Collecting relevant data regarding each individual in the population,according to predetermined parameters.2. Providing a superspreading score to each of the individuals accordingto a formula using the predetermined parameters.3. Ordering each individual according to his or her superspreading scorefrom high to low.4. Optionally dividing all individuals in groups according to theirsuperspreading score.

In some embodiments, after the list is ready, optionally in groups:

5. Notifying the individuals with a location and a time to receive thetreatments.6. Treating the population according to their superspreading score,optionally by groups, where individuals and/or groups hiving the higherscores will receive first the treatments. In some embodiments of theinvention, treatment is rather testing, as testing superspreaders may bea faster and more effective way of detecting a resurging pandemic.

Exemplary System

In some embodiments, the system comprises a computer networkarchitecture optionally with machine learning and/or other artificialintelligence tools to allow for the automated prioritization oftreatments in a pandemic event. In some embodiments, the system allowsfor prioritization of treatments using information regarding subjects ina population, disease process and progression, number of availabletreatment doses, and a plurality physical location attributes. In someembodiments, this potentially enables relevant authorities to measure,predict and/or improve their health-related performance during apandemic. In some embodiments, this in turn enables relevantdecision-making personnel and healthcare providers to get a truequantitative sense of what is possible to achieve with any givenpopulation of patients, in view of the parameters that define eachindividual and the population.

The following is an example of the workflow of a user experience with asystem of the present invention:

1. A user makes a request for an analysis and list generation report tothe system.2. The system uses an analytics module (A.M.) to analyze the informationof the population (for example, information as disclosed above).3. The system automatically issues a request to a Database Module (DB.M)to provide all relevant information and/or issues a request to externalsources (see above) to provide the required information and/or issues arequest to a simulations module (S.M) to perform the necessarysimulations.4. The analytics module (A.M.) collates the results into a unifiedanalysis response, based on any combination of the subjects in thepopulation and factors and/or components data available. In someembodiments of the invention, the A.M includes a ML module (optionallyin the form of an analytic system or a neural network) which is used topredict transmission and super-spreader potential of an individual basedon their past behavior. Optionally, an initial model is provided forsuch mapping. Optionally, the ML module also receives actual infectioninformation, for example, by automated collection from medical recordsor from epidemiological studies (e.g., of some or all infected people)and uses this information to update the model, for example, using amachine learning method as known in the art, to generate a prediction ofinfectiveness (and/or superspreader potential) of an individual givenhis contacts and the superspreader potential of similar individuals. Insome embodiments of the invention, statistical methods are used insteadof or in addition to ML methods. Optionally or additionally, what iscreated is a classifier, which classifies an individual as a potentialsuperspreader. Such a classifier can build a classification scheme givena set of individuals, each with behaviors and actual infectiveness asdetermined, for example, using epidemiological studies and/or contacttracking combined with disease detection in such tracked contacts. Suchclassifier may be used (or transmitted to individual devices to be usedinstead of and/or in addition to counting for example as describedherein) to generate a general score for an individual based on theclassification and optionally based on additional information, such asmedical risk.

Optionally or additionally, the AM includes one or more optimizationtools which given the various inputs described herein and/or one or moregoals, optimizes vaccine delivery and/or schedule to achieve a betterapproach to the goal.

5. The analytics module (A.M.) serves the response back to the system,and transmits the list to the user, and the list is now available to therelevant personnel. In some embodiments, this potentially helps therelevant personnel to decide whom, when and where distribute theavailable doses to the population.

Each and any of such modules may be implemented, for example, using acentral server, a distributed server and/or a cloud implementation.

In some embodiments, the system may automatically use the simulationmodels to select and apply a predictive model for the preferreddeployment of the doses (for example, the parameter may be number ofavailable doses or the higher number of individuals protected by the actof vaccination and/or a total number of expected of deaths and/or timeto reach a threshold where one or more limitations on society may beremoved). In some embodiments, the system may then predict theperformance of an underperforming vaccination result (if no changes aremade to trend performance) and predict the performance of the sametreatment result if the requirements are met, and then compare thebefore and after predicted performance to show the impact of meeting therequirements. A report of the requirements and of the predicted impactsof meeting the requirements may then be prepared by the system, andtransmitted to the user.

FIG. 7 schematically illustrates components of an exemplary systemcomprising a computer network architecture usable in some embodiments ofthe invention, comprising at least one optional server 702, an optionalanalytics module (A.M.) 704, an optional Database Module (DB.M) 706,and/or optional access to various third-party databases and sources 708,and an optional simulations module 712.

In some embodiments, a user using a user device 710 accesses the atleast one server 702. In some embodiments, the user transmits a userrequest to the analytics module (A.M.) 704 for analysis of data and thegeneration of a list 716. In some embodiments, analytics module (A.M.)704 accesses the Database Module (DB.M) 706 either directly and/or viathe server 702. In some embodiments, the analytics module (A.M.) 704accesses through various identified third party and sources 708. In someembodiments, data accessed from third-party databases and sources 708may be analyzed and stored in Database Module (DB.M) 706, thussupporting the simulations module 712, which performs machine-learningprediction activities. In some embodiments, the analytics module (A.M.)704 may also access data received from the simulations module 712 andpreviously stored in the Database Module (DB.M) 706, thus benefitingfrom the machine learning and artificial intelligence of the simulationsmodule 712.

In some embodiments, the system optionally comprises a prediction module714 with a prediction generator and in communication with the simulationmodule 712 and with the database module 706.

Not shown is a vaccination management server, which is optionally aseparate component of the system or be a separate system. In someembodiments of the invention, this server is used to manage distributionof vaccinations (e.g., locations and/or times) and/or tracking ofsubjects that requested vaccination and/or received such vaccination.Optionally, this server manages the logistics of vaccine distributionusing the information form the system indicating which subjects are tobe vaccinated and in what order. In some embodiments of the invention,vaccinations are distributed based on population density and thevaccination management server is used to track subjects receivingvaccinations to ensure that they are not vaccinated out of turn, forexample, by comparing prioritization data provided by the devicesagainst a record of prioritization intentions.

In some embodiments, the system allows automatic machine learning as newdata sources are added, and new data is collected, and the predictivealgorithms are recalibrated and reselected using the expanded, and hencemore reliable, data. In some embodiments, this may potentially enableusers of the system to quickly realize the value of new data.

In some embodiments, the system utilizes machine learning, optionallyincorporated in predictive model algorithms to execute predictiveanalytical operations. Learning may be supervised or unsupervised. Ingeneral, a predictive model analyzes historical data to identifypatterns in the data. The patterns identified may include relationshipsbetween various events, characteristics, or other attributes of the databeing analyzed. Modeling of such patterns may provide a predictive modelwhereby predictions may be made. Development of predictive models mayemploy mathematical or statistical modeling techniques such as curvefitting, smoothing, and regression analysis to fit or train the data.Such techniques may be used to model the distribution and relationshipsof the variables, e.g., how one or more events, characteristics, orcircumstances (which may be referred to as “independent variables” or“predictor variables”) relate to an event or outcome (which may bereferred to as a “dependent variable” or “response”).

In some embodiments, a machine learning process may include developing apredictive model. For example, a dataset comprising observed data may beinput into a modeling process for mapping of the variables within thedata. The mapped data may be used to develop a predictive model. Themachine learning process may also include utilizing the predictive modelto make predictions regarding a specified outcome that is a dependentvariable with respect to the predictive model. The machine may then beprovided an input of one or more observed predictor variables upon whichthe output or response is requested. By executing the machine-learningalgorithm utilizing the input, the requested response may be generatedand outputted. Thus, based on the presence or occurrence of a knownpredictor variable, the machine-learning algorithm may be used topredict a related future event or the probability of the future event.

It is noted that a most basic prediction may be used, e.g., behavior inpast predicts behavior in future. For example, if a person regularlymeets 30 people a day for over 15 minutes each and within 2 meters and Ia location that is closed (e.g., based on mapping data sources), it isassumed that may continue. Similarly, if a person attends a church of200 people once a week, that may be assumed to continue. In addition,class behavior may be applied. For example, if the person is collageage, the system may be programmed with an expectation of a certainnumber and/or expected dates and/or expected probability of parties sucha person might attend. Such information may also be generate bystatistically analyzing the behavior of others in that person′ cohort.

In some embodiments, once the treatment order list 716 is ready,individual messages 718 are sent to the specific individuals notifyingthem where and when they should go to be treated.

The architecture of the system may depend on the implementation. Forexample, if the system is mainly anonymous, with scorings beinggenerated on individual cellphones (or other devices), the server may beused to generate information to be used by the cellphones and/or tocollate results generate vaccination prioritization plans and/or inviteindividuals to be vaccinated.

In such an example, the software of the electronic device may increasein relative importance. Such device may include a memory (e.g., as notedherein) for storing actual IDs or partial IDs and/or counts thereof.Optionally or additionally, such device includes an ID generator.Optionally or additionally, such device includes communication software(e.g., addresses) for making an anonymous drop of information and/or forreceiving a general broadcast of information (e.g., from the server)and/or for accessing an individual's EMR or other repository withrelevant medical information. Optionally or additionally, such a deviceincludes a count analysis and/or other module that applies aclassification or scoring method for example, as described herein.Optionally or additionally, such a device includes a sensor anassociated software for detecting infection related information, forexample, being indoors, location, distance from other electronicdevices, duration at such distance, coughing sounds and/or video orstill analysis to detect mask wearing. Optionally or additionally, sucha device includes a display and associated software for showing avaccination invitation and/or a score. Optionally or additionally, sucha device includes an input (e.g., a camera) for receiving informationform printed or other screens, for example, a user's occupation orspecial dispensation. Optionally or additionally, such device includessoftware, which generates behavior alerts to the user, for example, whenthe user engages in riskier behavior.

Various embodiments and aspects of the present invention as delineatedhereinabove and as claimed in the claims section below find calculatedsupport in the following examples.

Example

Reference is now made to the following prophetic examples, whichtogether with the above descriptions illustrate some embodiments of theinvention in a non limiting fashion.

In the following example, three imaginary individuals (John Doe, JaneSmith and Mark Lite) will be scored according to one or more exemplaryfactors and/or components, as disclosed above. It should be understoodthat the following scenario is not limiting and it is only provided toenable a person having skills in the art to implement the invention.

Background Information

John Doe Jane Smith Mark Lite Age (relative weight 1%) 30 35 33Profession (relative Teacher Operator Unemployed weight 5%) Known healthconditions None Chronic coughing None (relative weight 4%) Visitsreligious gathering No Yes Yes (relative weight 20%)

Weekly Mobility Data

John Doe Jane Smith Mark Lite Day 1 Total locations visited: 5 Totallocations visited: 3 Total locations visited: 1 Estimated potentialEstimated potential Estimated potential number of individuals number ofindividuals number of individuals in contact with subject in contactwith subject in contact with subject on this day: 650 on this day: 150on this day: 5 Day 2 Total locations visited: 6 Total locations visited:4 Total locations visited: 1 Estimated potential Estimated potentialEstimated potential number of individuals number of individuals numberof individuals in contact with subject in contact with subject incontact with subject on this day: 750 on this day: 250 on this day: 5Day 3 Total locations visited: 5 Total locations visited: 2 Totallocations visited: 2 Estimated potential Estimated potential Estimatedpotential number of individuals number of individuals number ofindividuals in contact with subject in contact with subject in contactwith subject on this day: 650 on this day: 80 on this day: 30 Day 4Total locations visited: 5 Total locations visited: 2 Total locationsvisited: 1 Estimated potential Estimated potential Estimated potentialnumber of individuals number of individuals number of individuals incontact with subject in contact with subject in contact with subject onthis day: 650 on this day: 80 on this day: 5 Day 5 Total locationsvisited: 5 Total locations visited: 3 Total locations visited: 2Estimated potential Estimated potential Estimated potential number ofindividuals number of individuals number of individuals in contact withsubject in contact with subject in contact with subject on this day: 650on this day: 150 on this day: 30 Day 6 Total locations visited: 5 Totallocations visited: 1 Total locations visited: 1 Estimated potentialEstimated potential Estimated potential number of individuals number ofindividuals number of individuals in contact with subject in contactwith subject in contact with subject on this day: 650 on this day: 5 onthis day: 5 Day 7 Total locations visited: 5 Total locations visited: 2Total locations visited: 3 Estimated potential (*visited Church)(*visited stadium) number of individuals Estimated potential Estimatedpotential in contact with subject number of individuals number ofindividuals on this day: 650 in contact with subject in contact withsubject on this day: 500 on this day: 500 Score 80 60 15 (relativeweight 70%)

In view of the results of the Weekly mobility data alone, the order ofthe treatments will be John Doe, Jane Smith and then Mark Lite.

The calculation of the overall score is:

criteria John Doe Jane Smith Mark Lite Age 1% 50 50 50 Profession 5% 8050 0 Known health conditions 4% 0 90 0 Visits religious gathering 20%  080 80 Mobility data 70%  80 60 15 weighted scores 100%  60.5 66.2 14.2

As can be seen, when taking under consideration all the informationdata, the order of the treatments will be Jane Smith, John Doe and thenMark Lite.

It should be understood that the above numeric examples are justexamples to help a person having skills in the art to understand theinvention. It also should be understood that different weight values,scores and methods of calculating a score could be used.

It is expected that during the life of a patent maturing from thisapplication many relevant parameters of scoring activity of individualsand methods of measuring said parameters will be developed; the scope ofthe invention herein is intended to include all such new technologies apriori.

As used herein with reference to quantity or value, the term “about”means “within ±20% of”.

The terms “comprises”, “comprising”, “includes”, “including”, “has”,“having” and their conjugates mean “including but not limited to”.

The term “consisting of” means “including and limited to”.

The term “consisting essentially of” means that the composition, methodor structure may include additional ingredients, steps and/or parts, butonly if the additional ingredients, steps and/or parts do not materiallyalter the basic and novel characteristics of the claimed composition,method or structure.

As used herein, the singular forms “a”, “an” and “the” include pluralreferences unless the context clearly dictates otherwise. For example,the term “a compound” or “at least one compound” may include a pluralityof compounds, including mixtures thereof.

Throughout this application, embodiments of this invention may bepresented with reference to a range format. It should be understood thatthe description in range format is merely for convenience and brevityand should not be construed as an inflexible limitation on the scope ofthe invention. Accordingly, the description of a range should beconsidered to have specifically disclosed all the possible subranges aswell as individual numerical values within that range. For example,description of a range such as “from 1 to 6” should be considered tohave specifically disclosed subranges such as “from 1 to 3”, “from 1 to4”, “from 1 to 5”, “from 2 to 4”, “from 2 to 6”, “from 3 to 6”, etc.; aswell as individual numbers within that range, for example, 1, 2, 3, 4,5, and 6. This applies regardless of the breadth of the range.

Whenever a numerical range is indicated herein (for example “10-15”, “10to 15”, or any pair of numbers linked by these another such rangeindication), it is meant to include any number (fractional or integral)within the indicated range limits, including the range limits, unlessthe context clearly dictates otherwise. The phrases“range/ranging/ranges between” a first indicate number and a secondindicate number and “range/ranging/ranges from” a first indicate number“to”, “up to”, “until” or “through” (or another such range-indicatingterm) a second indicate number are used herein interchangeably and aremeant to include the first and second indicated numbers and all thefractional and integral numbers therebetween.

Unless otherwise indicated, numbers used herein and any number rangesbased thereon are approximations within the accuracy of reasonablemeasurement and rounding errors as understood by persons skilled in theart.

It is appreciated that certain features of the invention, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable subcombination or as suitable in any other describedembodiment of the invention. Certain features described in the contextof various embodiments are not to be considered essential features ofthose embodiments, unless the embodiment is inoperative without thoseelements.

It is the intent of the applicant(s) that all publications, patents andpatent applications referred to in this specification are to beincorporated in their entirety by reference into the specification, asif each individual publication, patent or patent application wasspecifically and individually noted when referenced that it is to beincorporated herein by reference. In addition, citation oridentification of any reference in this application shall not beconstrued as an admission that such reference is available as prior artto the present invention. To the extent that section headings are used,they should not be construed as necessarily limiting. In addition, anypriority document(s) of this application is/are hereby incorporatedherein by reference in its/their entirety.

What is claimed is:
 1. An anonymized method of treating subjects againstan infectious disease caused by a pathogen, comprising: a. providing anelectronic device with proximity tracking circuitry for each of saidsubjects; b. generating an ID for each said electronic device; c. at aproximity event, when a particular said electronic device of aparticular said subject is in proximity of one or more other of saidelectronic devices, one or both of transmitting said ID or an indicationthereof to said one or more other devices and receiving an ID orindication thereof from said one or more other devices, by saidparticular electronic device; d. generating, by said particularelectronic device a score reflecting a propensity for proximity,according to a plurality of received IDs; e. generating for saidparticular electronic device a prioritization of treatment based on saidscore; and f. treating said particular subject according to saidprioritization.
 2. The method according to claim 1, wherein saidgenerating an ID comprises generating an ID having fewer than 100,000potential values.
 3. The method according to claim 2, wherein saidgenerating an ID comprises generating a unique ID and also generatingsaid ID as a portion of said unique ID.
 4. The method according to claim1, further comprising changing said ID periodically.
 5. The methodaccording to claim 1, further comprising generating a second ID andtransmitting said second ID or indication thereof together with said ID.6. The method according to claim 5, wherein said transmitting a secondID is carried out only at a fraction of said proximity events.
 7. Themethod according to claim 6, wherein said transmitting comprisestransmitting also second IDs previously received from others of saidelectronic devices.
 8. The method according to claim 6, comprisinggenerating an indication of closeness of a population met by saidelectronic device based on said received second IDs.
 9. The methodaccording to claim 1, wherein said score depends on an estimation ofpropensity of proximity of said one or more other devices.
 10. Themethod according to claim 1, wherein said generating said scorecomprises counting the number of received IDs.
 11. The method accordingto claim 10, wherein said counting comprises counting unique IDs. 12.The method according to claim 10, wherein said counting comprisescounting IDs with a weighted parameter, said weighted parameter isgenerated by analyzing said exchanged second IDs.
 13. The methodaccording to claim 1, wherein said generating for said particular devicecomprises transmitting said score to a server and generating saidprioritization on said server.
 14. The method according to claim 13,wherein generating said prioritization comprises comparing scores bydifferent ones of said electronic devices.
 15. The method according toclaim 1, wherein said generating for said particular device comprisesgenerating said prioritization on said particular electronic device. 16.The method according to claim 15, wherein said generation comprisesreceiving form a server a list or a function indication prioritizationaccording to score.
 17. The method according to claim 1, comprisingdisplaying treatment instructions on said particular electronic devicebased on said generated prioritization.
 18. The method of claim 1,wherein said pathogen comprises a corona virus and wherein saidtreatment comprises a vaccination and wherein said prioritization isused to select subjects at greater risk of transmitting the pathogenduring a pandemic to be vaccinated sooner than subjects less likely totransmit the pathogen.
 19. A system for anonymously selecting subjectsfor treatment against an infectious disease caused by a pathogen,comprising: a. a plurality of electronic devices configured to becarried around by said subjects and configured with instructions to: i.generate an ID comprising for each said electronic device; ii. when inproximity of another such electronic device, one or both of transmitsaid ID or an indication thereof to said another electronic device andreceive an ID or indication thereof from said another electronic device;iii. generating, a score based on a plurality of such received IDs; iv.receiving information from a server; v. displaying relevant treatmentinstructions to said subjects based on said received information; b. atleast one server comprising a memory and a plurality of modules; saidmemory comprising instructions for: vii. sending to said plurality ofelectronic devices information usable by a circuitry in said pluralityof electronic devices to display said relevant treatment instructions,wherein said at least one server or said electronic devices compriseinstructions to generate a prediction of likelihood of a subjecttransmitting said pathogen, based on a score of the subject.
 20. Thesystem according to claim 19, wherein said information comprises one ormore of subject specific information.
 21. The system according to claim19, wherein said information comprises general information usable by aplurality of subjects and devices thereof.
 22. The system according toclaim 19, wherein said server is configured with instructions to receiveanonymous scores for a plurality of said electronic devices and use saidreceived scores to generate said general information, said electronicdevices configured to use said general information to determine arelative treatment priority for their respective subjects.
 23. Thesystem according to claim 19, wherein said electronic devices comprisesa proximity-detecting module using one or more of: a. physical proximitydata received by means of electronic positioning data of said subject;b. a distance indicating sensor which indicates physical proximity ofthe location of a device in relation to the location of said anotherdevice; and c. historical location data.
 24. The system according toclaim 19, wherein said at least one server or said electronic devicescomprise instructions to determine a treatment prioritization based onsaid likelihood.
 25. The system according to claim 23, wherein saiddetermine a treatment prioritization further comprises one or more of:a. generating a score component based on a nature of a location wheresaid physical proximity data is related; b. generating a score componentcomprising health data of the subject of one or both electronic devices;c. generating a score component comprising a profession of the subjectof one or both electronic devices; d. generating a score componentreflecting relative health risk to said subject if said subjectcontracts said pathogen; and e. generating a score component reflectingdamage to society if said subject contracts said pathogen.
 26. Thesystem according to claim 23, wherein when said physical proximity datais related to a location that is either indoors or in a closed space,then said predicted likelihood of said subject of transmitting saidpathogen increases by a factor of between about 10 times to about 100times.
 27. The system according to claim 19, further comprising avaccination server which allocates vaccinations for a corona virusaccording to said displayed treatment information.
 28. The systemaccording to claim 27, wherein said server comprises a simulation moduleconfigured to perform one or both of: (a) predict the effect ofvaccination on disease spread; (b) predict the effect of an IDtransmission probability on distinguishing between subjects who contactmainly subjects in a same subpopulation.
 29. The system of claim 19,wherein said electronic devices are configured to transmit a second IDand previously received second IDs, at a probability of less than 10%and using said received second IDs to generate said score.
 30. Thesystem of claim 19, wherein said transmitted ID is a non-unique IDhaving fewer possible values than 10% of the number of said devices.